{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# EDA Example\n",
    "对Springleaf数据集进行数据清洗"
   ],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# 基础库\n",
    "# 前两行画图\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 系统库\n",
    "import os, sys\n",
    "\n",
    "# 自带数据\n",
    "datalib_path = os.path.join(os.path.abspath('.'), '../')\n",
    "sys.path.append(datalib_path)\n",
    "import dataset\n",
    "\n",
    "# 忽略warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# import seaborn"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "def autolabel(arrayA):\n",
    "    ''' label each colored square with the corresponding data value. \n",
    "    If value > 20, the text is in black, else in white.\n",
    "    '''\n",
    "    arrayA = np.array(arrayA)\n",
    "    for i in range(arrayA.shape[0]):\n",
    "        for j in range(arrayA.shape[1]):\n",
    "                plt.text(j,i, \"%.2f\"%arrayA[i,j], ha='center', va='bottom',color='w')\n",
    "\n",
    "def hist_it(feat):\n",
    "    plt.figure(figsize=(16,4))\n",
    "    feat[Y_train==0].hist(bins=range(int(feat.min()),int(feat.max()+2)),normed=True,alpha=0.8)\n",
    "    feat[Y_train==1].hist(bins=range(int(feat.min()),int(feat.max()+2)),normed=True,alpha=0.5)\n",
    "    plt.ylim((0,1))\n",
    "    \n",
    "def gt_matrix(feats,sz=16):\n",
    "    a = []\n",
    "    for i,c1 in enumerate(feats):\n",
    "        b = [] \n",
    "        for j,c2 in enumerate(feats):\n",
    "            mask = (~X_train[c1].isnull()) & (~X_train[c2].isnull())\n",
    "            if i>=j:\n",
    "                b.append((X_train.loc[mask,c1].values>=X_train.loc[mask,c2].values).mean())\n",
    "            else:\n",
    "                b.append((X_train.loc[mask,c1].values>X_train.loc[mask,c2].values).mean())\n",
    "\n",
    "        a.append(b)\n",
    "\n",
    "    plt.figure(figsize = (sz,sz))\n",
    "    plt.imshow(a, interpolation = 'None')\n",
    "    _ = plt.xticks(range(len(feats)),feats,rotation = 90)\n",
    "    _ = plt.yticks(range(len(feats)),feats,rotation = 0)\n",
    "    autolabel(a)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def hist_it1(feat):\n",
    "    plt.figure(figsize=(16,4))\n",
    "    feat[Y==0].hist(bins=100,range=(feat.min(),feat.max()),normed=True,alpha=0.5)\n",
    "    feat[Y==1].hist(bins=100,range=(feat.min(),feat.max()),normed=True,alpha=0.5)\n",
    "    plt.ylim((0,1))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# 载入数据\n",
    "# 需要2分钟\n",
    "train_raw = pd.read_csv(os.path.join(dataset.springleaf_path, 'train.csv.zip'))\n",
    "train_target_raw = train_raw.target\n",
    "train_raw.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>VAR_0001</th>\n",
       "      <th>VAR_0002</th>\n",
       "      <th>VAR_0003</th>\n",
       "      <th>VAR_0004</th>\n",
       "      <th>VAR_0005</th>\n",
       "      <th>VAR_0006</th>\n",
       "      <th>VAR_0007</th>\n",
       "      <th>VAR_0008</th>\n",
       "      <th>VAR_0009</th>\n",
       "      <th>...</th>\n",
       "      <th>VAR_1926</th>\n",
       "      <th>VAR_1927</th>\n",
       "      <th>VAR_1928</th>\n",
       "      <th>VAR_1929</th>\n",
       "      <th>VAR_1930</th>\n",
       "      <th>VAR_1931</th>\n",
       "      <th>VAR_1932</th>\n",
       "      <th>VAR_1933</th>\n",
       "      <th>VAR_1934</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>4300</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>H</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>4448</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>H</td>\n",
       "      <td>116</td>\n",
       "      <td>3</td>\n",
       "      <td>3464</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>H</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>3200</td>\n",
       "      <td>C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>RCC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>R</td>\n",
       "      <td>72</td>\n",
       "      <td>261</td>\n",
       "      <td>2000</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>BRANCH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1934 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID VAR_0001  VAR_0002  VAR_0003  VAR_0004 VAR_0005  VAR_0006  VAR_0007  \\\n",
       "0   2        H       224         0      4300        C       0.0       0.0   \n",
       "1   4        H         7        53      4448        B       1.0       0.0   \n",
       "2   5        H       116         3      3464        C       0.0       0.0   \n",
       "3   7        H       240       300      3200        C       0.0       0.0   \n",
       "4   8        R        72       261      2000        N       0.0       0.0   \n",
       "\n",
       "  VAR_0008 VAR_0009  ... VAR_1926 VAR_1927 VAR_1928   VAR_1929  VAR_1930  \\\n",
       "0    False    False  ...       98       98      998  999999998       998   \n",
       "1    False    False  ...       98       98      998  999999998       998   \n",
       "2    False    False  ...       98       98      998  999999998       998   \n",
       "3    False    False  ...       98       98      998  999999998       998   \n",
       "4    False    False  ...       98       98      998  999999998       998   \n",
       "\n",
       "   VAR_1931  VAR_1932  VAR_1933  VAR_1934  target  \n",
       "0       998      9998      9998      IAPS       0  \n",
       "1       998      9998      9998      IAPS       0  \n",
       "2       998      9998      9998      IAPS       0  \n",
       "3       998      9998      9998       RCC       0  \n",
       "4       998      9998      9998    BRANCH       1  \n",
       "\n",
       "[5 rows x 1934 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# 需要2分钟\n",
    "test_raw = pd.read_csv(os.path.join(dataset.springleaf_path, 'test.csv.zip'))\n",
    "test_ID = test_raw.ID\n",
    "test_raw.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>VAR_0001</th>\n",
       "      <th>VAR_0002</th>\n",
       "      <th>VAR_0003</th>\n",
       "      <th>VAR_0004</th>\n",
       "      <th>VAR_0005</th>\n",
       "      <th>VAR_0006</th>\n",
       "      <th>VAR_0007</th>\n",
       "      <th>VAR_0008</th>\n",
       "      <th>VAR_0009</th>\n",
       "      <th>...</th>\n",
       "      <th>VAR_1925</th>\n",
       "      <th>VAR_1926</th>\n",
       "      <th>VAR_1927</th>\n",
       "      <th>VAR_1928</th>\n",
       "      <th>VAR_1929</th>\n",
       "      <th>VAR_1930</th>\n",
       "      <th>VAR_1931</th>\n",
       "      <th>VAR_1932</th>\n",
       "      <th>VAR_1933</th>\n",
       "      <th>VAR_1934</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>360</td>\n",
       "      <td>25</td>\n",
       "      <td>2251</td>\n",
       "      <td>B</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>R</td>\n",
       "      <td>74</td>\n",
       "      <td>192</td>\n",
       "      <td>3274</td>\n",
       "      <td>C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>R</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>3500</td>\n",
       "      <td>C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>R</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>B</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>H</td>\n",
       "      <td>91</td>\n",
       "      <td>39</td>\n",
       "      <td>84500</td>\n",
       "      <td>C</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>998</td>\n",
       "      <td>998</td>\n",
       "      <td>9998</td>\n",
       "      <td>9998</td>\n",
       "      <td>IAPS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1933 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID VAR_0001  VAR_0002  VAR_0003  VAR_0004 VAR_0005  VAR_0006  VAR_0007  \\\n",
       "0   1        R       360        25      2251        B       2.0       2.0   \n",
       "1   3        R        74       192      3274        C       2.0       3.0   \n",
       "2   6        R        21        36      3500        C       1.0       1.0   \n",
       "3   9        R         8         2      1500        B       0.0       0.0   \n",
       "4  10        H        91        39     84500        C       8.0       3.0   \n",
       "\n",
       "  VAR_0008 VAR_0009  ... VAR_1925 VAR_1926 VAR_1927  VAR_1928   VAR_1929  \\\n",
       "0    False    False  ...        0       98       98       998  999999998   \n",
       "1    False    False  ...        0       98       98       998  999999998   \n",
       "2    False    False  ...        0       98       98       998  999999998   \n",
       "3    False    False  ...        0       98       98       998  999999998   \n",
       "4    False    False  ...        0       98       98       998  999999998   \n",
       "\n",
       "   VAR_1930  VAR_1931  VAR_1932  VAR_1933  VAR_1934  \n",
       "0       998       998      9998      9998      IAPS  \n",
       "1       998       998      9998      9998      IAPS  \n",
       "2       998       998      9998      9998      IAPS  \n",
       "3       998       998      9998      9998      IAPS  \n",
       "4       998       998      9998      9998      IAPS  \n",
       "\n",
       "[5 rows x 1933 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# 数据规模\n",
    "# 行数，列数\n",
    "print('Train shape', train_raw.shape)\n",
    "print('Test shape', test_raw.shape)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Train shape (145231, 1934)\n",
      "Test shape (145232, 1933)\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# 压缩规模，取前5000条和前1000条\n",
    "X_train = train_raw[:5000]\n",
    "Y_train = train_raw.target[0:5000]\n",
    "X_test = test_raw[:1000]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# 列出所有含有null值的行\n",
    "X_train.isnull().sum(axis=1).head(15)  "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0     25\n",
       "1     19\n",
       "2     24\n",
       "3     24\n",
       "4     24\n",
       "5     24\n",
       "6     24\n",
       "7     24\n",
       "8     16\n",
       "9     24\n",
       "10    22\n",
       "11    24\n",
       "12    17\n",
       "13    24\n",
       "14    24\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# 列出所有含有null值的列\n",
    "X_train.isnull().sum(axis=0).head(15)  "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ID          0\n",
       "VAR_0001    0\n",
       "VAR_0002    0\n",
       "VAR_0003    0\n",
       "VAR_0004    0\n",
       "VAR_0005    0\n",
       "VAR_0006    0\n",
       "VAR_0007    0\n",
       "VAR_0008    0\n",
       "VAR_0009    0\n",
       "VAR_0010    0\n",
       "VAR_0011    0\n",
       "VAR_0012    0\n",
       "VAR_0013    0\n",
       "VAR_0014    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 数据清洗\n",
    "通过对数据的分析，删除无用的列和行"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# 将训练集和测试集统一处理\n",
    "X_train['is_train'] = 1\n",
    "X_test['is_train'] = 0\n",
    "\n",
    "# 利用concat将两个数据集合起来\n",
    "# 注意OOM，concat函数会复制数据，内存占用会变成之前的两倍\n",
    "X_traintest = pd.concat([X_train, X_test], axis = 0)\n",
    "X_traintest.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(6000, 1935)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# 统计每个特征的值\n",
    "# `dropna = False` makes nunique treat NaNs as a distinct value\n",
    "feats_counts = X_train.nunique(dropna = False)  \n",
    " # 列出只有一个值的列\n",
    "feats_counts.sort_values()[:10] "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "is_train    1\n",
       "VAR_0038    1\n",
       "VAR_0039    1\n",
       "VAR_0040    1\n",
       "VAR_0223    1\n",
       "VAR_0042    1\n",
       "VAR_0043    1\n",
       "VAR_0044    1\n",
       "VAR_0188    1\n",
       "VAR_0189    1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# 找到全部的唯一值的列\n",
    "# 他们对于modeling没有帮助\n",
    "constant_features = [c for c in feats_counts.loc[feats_counts==1].index.tolist() if c != 'is_train']\n",
    "print(constant_features)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['VAR_0008', 'VAR_0009', 'VAR_0010', 'VAR_0011', 'VAR_0012', 'VAR_0018', 'VAR_0019', 'VAR_0020', 'VAR_0021', 'VAR_0022', 'VAR_0023', 'VAR_0024', 'VAR_0025', 'VAR_0026', 'VAR_0027', 'VAR_0028', 'VAR_0029', 'VAR_0030', 'VAR_0031', 'VAR_0032', 'VAR_0038', 'VAR_0039', 'VAR_0040', 'VAR_0041', 'VAR_0042', 'VAR_0043', 'VAR_0044', 'VAR_0106', 'VAR_0188', 'VAR_0189', 'VAR_0190', 'VAR_0191', 'VAR_0192', 'VAR_0193', 'VAR_0196', 'VAR_0197', 'VAR_0199', 'VAR_0202', 'VAR_0203', 'VAR_0207', 'VAR_0213', 'VAR_0214', 'VAR_0215', 'VAR_0216', 'VAR_0221', 'VAR_0222', 'VAR_0223', 'VAR_0229', 'VAR_0239', 'VAR_0840', 'VAR_0847', 'VAR_1428']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# 删除没用的列\n",
    "# inplace是原位操作，节省内存，但是对原始数据有改动\n",
    "X_traintest.drop(constant_features,axis = 1,inplace=True)\n",
    "X_traintest.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(6000, 1883)"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# 空值填充\n",
    "X_train.fillna('NaN', inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now let's encode each feature, as we discussed. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "X_train['VAR_0002'].factorize()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(array([  0,   1,   2, ...,  28,  26, 274]),\n",
       " Int64Index([224,   7, 116, 240,  72,   4,  60,  13,  17,  24,\n",
       "             ...\n",
       "             235, 447, 369, 706, 500, 760, 497, 443, 481, 462],\n",
       "            dtype='int64', length=456))"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "# 对每个列进行编码\n",
    "# 生成mapping\n",
    "X_train_enc = pd.DataFrame(index = X_train.index)\n",
    "for col in X_traintest.columns:\n",
    "    X_train_enc[col] = X_train[col].factorize()[0]"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "# 另外一种方式\n",
    "# train_enc[col] = train[col].map(train[col].value_counts())"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "# The resulting data frame is very very large, so we cannot just transpose it and use .duplicated. That is why we will use a simple loop.\n",
    "# 查找重复列\n",
    "# 需要10分钟\n",
    "dup_cols = {}\n",
    "\n",
    "for i, c1 in enumerate(X_train_enc.columns):\n",
    "    for c2 in X_train_enc.columns[i + 1:]:\n",
    "        if c2 not in dup_cols and np.all(X_train_enc[c1] == X_train_enc[c2]):\n",
    "            dup_cols[c2] = c1"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# 重复列的mapping\n",
    "dup_cols"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'VAR_0227': 'ID',\n",
       " 'VAR_0228': 'ID',\n",
       " 'VAR_0013': 'VAR_0006',\n",
       " 'VAR_0201': 'VAR_0051',\n",
       " 'VAR_0238': 'VAR_0089',\n",
       " 'VAR_0114': 'VAR_0098',\n",
       " 'VAR_0130': 'VAR_0098',\n",
       " 'VAR_0115': 'VAR_0099',\n",
       " 'VAR_0181': 'VAR_0180',\n",
       " 'VAR_0182': 'VAR_0180',\n",
       " 'VAR_0195': 'VAR_0194',\n",
       " 'VAR_0210': 'VAR_0208',\n",
       " 'VAR_0211': 'VAR_0208',\n",
       " 'VAR_0394': 'VAR_0246',\n",
       " 'VAR_0395': 'VAR_0246',\n",
       " 'VAR_0396': 'VAR_0246',\n",
       " 'VAR_0397': 'VAR_0246',\n",
       " 'VAR_0398': 'VAR_0246',\n",
       " 'VAR_0399': 'VAR_0246',\n",
       " 'VAR_0411': 'VAR_0246',\n",
       " 'VAR_0412': 'VAR_0246',\n",
       " 'VAR_0438': 'VAR_0246',\n",
       " 'VAR_0445': 'VAR_0246',\n",
       " 'VAR_0446': 'VAR_0246',\n",
       " 'VAR_0449': 'VAR_0246',\n",
       " 'VAR_0459': 'VAR_0246',\n",
       " 'VAR_0463': 'VAR_0246',\n",
       " 'VAR_0526': 'VAR_0246',\n",
       " 'VAR_0527': 'VAR_0246',\n",
       " 'VAR_0528': 'VAR_0246',\n",
       " 'VAR_0529': 'VAR_0246',\n",
       " 'VAR_0530': 'VAR_0246',\n",
       " 'VAR_0357': 'VAR_0260',\n",
       " 'VAR_0512': 'VAR_0506',\n",
       " 'VAR_0671': 'VAR_0669',\n",
       " 'VAR_0672': 'VAR_0670',\n",
       " 'VAR_1036': 'VAR_0916',\n",
       " 'VAR_1628': 'VAR_1620',\n",
       " 'VAR_1849': 'VAR_1844',\n",
       " 'VAR_1846': 'VAR_1845',\n",
       " 'VAR_1847': 'VAR_1845',\n",
       " 'VAR_1850': 'VAR_1845'}"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "# 该计算需要很长时间\n",
    "# 可以保存起来，下次使用\n",
    "# import pickle\n",
    "# pickle.dump(dup_cols, open('dup_cols.p', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "# 删除重复列\n",
    "X_traintest.drop(dup_cols.keys(), axis = 1,inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "# 唯一值统计\n",
    "nunique = X_train.nunique(dropna=False)\n",
    "nunique"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ID          5000\n",
       "VAR_0001       3\n",
       "VAR_0002     456\n",
       "VAR_0003     385\n",
       "VAR_0004    1929\n",
       "            ... \n",
       "VAR_1932      17\n",
       "VAR_1933     154\n",
       "VAR_1934       5\n",
       "target         2\n",
       "is_train       1\n",
       "Length: 1935, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "# 计算唯一值的数据占比\n",
    "plt.figure(figsize=(14,6))\n",
    "_ = plt.hist(nunique.astype(float)/X_train.shape[0], bins=100)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# 找到唯一值占比很大（即重复性少）的列\n",
    "# 这些值基本都是整数，意味着他们是某种ID或者统计量（count）\n",
    "mask = (nunique.astype(float)/X_train.shape[0] > 0.8)\n",
    "X_train.loc[:, mask]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>VAR_0212</th>\n",
       "      <th>VAR_0227</th>\n",
       "      <th>VAR_0228</th>\n",
       "      <th>VAR_0541</th>\n",
       "      <th>VAR_0543</th>\n",
       "      <th>VAR_0704</th>\n",
       "      <th>VAR_0887</th>\n",
       "      <th>VAR_0899</th>\n",
       "      <th>VAR_1081</th>\n",
       "      <th>VAR_1082</th>\n",
       "      <th>VAR_1087</th>\n",
       "      <th>VAR_1179</th>\n",
       "      <th>VAR_1180</th>\n",
       "      <th>VAR_1181</th>\n",
       "      <th>VAR_1495</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>311951.0</td>\n",
       "      <td>311951.0</td>\n",
       "      <td>49463</td>\n",
       "      <td>116783</td>\n",
       "      <td>25619</td>\n",
       "      <td>19214</td>\n",
       "      <td>112871</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>116783</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>76857</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.20713e+10</td>\n",
       "      <td>2769488.0</td>\n",
       "      <td>2769488.0</td>\n",
       "      <td>303472</td>\n",
       "      <td>346196</td>\n",
       "      <td>28336</td>\n",
       "      <td>28336</td>\n",
       "      <td>346375</td>\n",
       "      <td>341365</td>\n",
       "      <td>341365</td>\n",
       "      <td>346196</td>\n",
       "      <td>341365</td>\n",
       "      <td>341365</td>\n",
       "      <td>176604</td>\n",
       "      <td>27542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2.65477e+10</td>\n",
       "      <td>654127.0</td>\n",
       "      <td>654127.0</td>\n",
       "      <td>94990</td>\n",
       "      <td>122601</td>\n",
       "      <td>35589</td>\n",
       "      <td>35589</td>\n",
       "      <td>121501</td>\n",
       "      <td>107267</td>\n",
       "      <td>107267</td>\n",
       "      <td>121501</td>\n",
       "      <td>107267</td>\n",
       "      <td>107267</td>\n",
       "      <td>58714</td>\n",
       "      <td>19238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7.75753e+10</td>\n",
       "      <td>3015088.0</td>\n",
       "      <td>3015088.0</td>\n",
       "      <td>20593</td>\n",
       "      <td>59490</td>\n",
       "      <td>5854</td>\n",
       "      <td>5204</td>\n",
       "      <td>61890</td>\n",
       "      <td>45794</td>\n",
       "      <td>47568</td>\n",
       "      <td>59490</td>\n",
       "      <td>45794</td>\n",
       "      <td>47568</td>\n",
       "      <td>47568</td>\n",
       "      <td>29182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6.04238e+10</td>\n",
       "      <td>118678.0</td>\n",
       "      <td>118678.0</td>\n",
       "      <td>10071</td>\n",
       "      <td>35708</td>\n",
       "      <td>2550</td>\n",
       "      <td>2266</td>\n",
       "      <td>34787</td>\n",
       "      <td>20475</td>\n",
       "      <td>23647</td>\n",
       "      <td>34708</td>\n",
       "      <td>20475</td>\n",
       "      <td>23647</td>\n",
       "      <td>23647</td>\n",
       "      <td>23932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4995</td>\n",
       "      <td>9725</td>\n",
       "      <td>3.20241e+10</td>\n",
       "      <td>2913739.0</td>\n",
       "      <td>2913739.0</td>\n",
       "      <td>50054</td>\n",
       "      <td>89996</td>\n",
       "      <td>16555</td>\n",
       "      <td>16555</td>\n",
       "      <td>92321</td>\n",
       "      <td>81099</td>\n",
       "      <td>81099</td>\n",
       "      <td>89996</td>\n",
       "      <td>81099</td>\n",
       "      <td>81099</td>\n",
       "      <td>42321</td>\n",
       "      <td>21348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4996</td>\n",
       "      <td>9730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1713017.0</td>\n",
       "      <td>1713017.0</td>\n",
       "      <td>227</td>\n",
       "      <td>1000</td>\n",
       "      <td>342</td>\n",
       "      <td>342</td>\n",
       "      <td>500</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>1000</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>2647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4997</td>\n",
       "      <td>9736</td>\n",
       "      <td>2.45404e+10</td>\n",
       "      <td>1564676.0</td>\n",
       "      <td>1564676.0</td>\n",
       "      <td>13943</td>\n",
       "      <td>15343</td>\n",
       "      <td>3844</td>\n",
       "      <td>3844</td>\n",
       "      <td>19193</td>\n",
       "      <td>14444</td>\n",
       "      <td>14959</td>\n",
       "      <td>15193</td>\n",
       "      <td>14444</td>\n",
       "      <td>14959</td>\n",
       "      <td>14959</td>\n",
       "      <td>13943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4998</td>\n",
       "      <td>9738</td>\n",
       "      <td>2.97304e+10</td>\n",
       "      <td>2400564.0</td>\n",
       "      <td>2400564.0</td>\n",
       "      <td>440</td>\n",
       "      <td>726</td>\n",
       "      <td>577</td>\n",
       "      <td>577</td>\n",
       "      <td>4290</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>726</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>505</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4999</td>\n",
       "      <td>9740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2696291.0</td>\n",
       "      <td>2696291.0</td>\n",
       "      <td>8159</td>\n",
       "      <td>19077</td>\n",
       "      <td>1967</td>\n",
       "      <td>1788</td>\n",
       "      <td>29792</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>18777</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>12106</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID     VAR_0212   VAR_0227   VAR_0228  VAR_0541  VAR_0543  VAR_0704  \\\n",
       "0        2          NaN   311951.0   311951.0     49463    116783     25619   \n",
       "1        4  9.20713e+10  2769488.0  2769488.0    303472    346196     28336   \n",
       "2        5  2.65477e+10   654127.0   654127.0     94990    122601     35589   \n",
       "3        7  7.75753e+10  3015088.0  3015088.0     20593     59490      5854   \n",
       "4        8  6.04238e+10   118678.0   118678.0     10071     35708      2550   \n",
       "...    ...          ...        ...        ...       ...       ...       ...   \n",
       "4995  9725  3.20241e+10  2913739.0  2913739.0     50054     89996     16555   \n",
       "4996  9730          NaN  1713017.0  1713017.0       227      1000       342   \n",
       "4997  9736  2.45404e+10  1564676.0  1564676.0     13943     15343      3844   \n",
       "4998  9738  2.97304e+10  2400564.0  2400564.0       440       726       577   \n",
       "4999  9740          NaN  2696291.0  2696291.0      8159     19077      1967   \n",
       "\n",
       "      VAR_0887  VAR_0899  VAR_1081  VAR_1082  VAR_1087  VAR_1179  VAR_1180  \\\n",
       "0        19214    112871     76857     76857    116783     76857     76857   \n",
       "1        28336    346375    341365    341365    346196    341365    341365   \n",
       "2        35589    121501    107267    107267    121501    107267    107267   \n",
       "3         5204     61890     45794     47568     59490     45794     47568   \n",
       "4         2266     34787     20475     23647     34708     20475     23647   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4995     16555     92321     81099     81099     89996     81099     81099   \n",
       "4996       342       500       227       227      1000       227       227   \n",
       "4997      3844     19193     14444     14959     15193     14444     14959   \n",
       "4998       577      4290       505       505       726       505       505   \n",
       "4999      1788     29792     12106     12106     18777     12106     12106   \n",
       "\n",
       "      VAR_1181  VAR_1495  \n",
       "0        76857     50000  \n",
       "1       176604     27542  \n",
       "2        58714     19238  \n",
       "3        47568     29182  \n",
       "4        23647     23932  \n",
       "...        ...       ...  \n",
       "4995     42321     21348  \n",
       "4996       227      2647  \n",
       "4997     14959     13943  \n",
       "4998       505       440  \n",
       "4999     12106     16000  \n",
       "\n",
       "[5000 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "# 再看一下重复率相对少的列\n",
    "# These look like counts too. \n",
    "# First thing to notice is the 23th line: 99999.., -99999 values look like NaNs so we should probably built a related feature. \n",
    "# Second: the columns are sometimes placed next to each other, so the columns are probably grouped together and we can disentangle that.      \n",
    "# 要注意的点：异常值（比如-99999或者+99999）可能代表着NaN或者缺失数据\n",
    "mask = (nunique.astype(float)/X_train.shape[0] < 0.8) & (nunique.astype(float)/X_train.shape[0] > 0.4)\n",
    "X_train.loc[:25, mask]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAR_0200</th>\n",
       "      <th>VAR_0241</th>\n",
       "      <th>VAR_0293</th>\n",
       "      <th>VAR_0313</th>\n",
       "      <th>VAR_0585</th>\n",
       "      <th>VAR_0609</th>\n",
       "      <th>VAR_0648</th>\n",
       "      <th>VAR_0649</th>\n",
       "      <th>VAR_0652</th>\n",
       "      <th>VAR_0705</th>\n",
       "      <th>...</th>\n",
       "      <th>VAR_1374</th>\n",
       "      <th>VAR_1489</th>\n",
       "      <th>VAR_1494</th>\n",
       "      <th>VAR_1496</th>\n",
       "      <th>VAR_1497</th>\n",
       "      <th>VAR_1801</th>\n",
       "      <th>VAR_1802</th>\n",
       "      <th>VAR_1809</th>\n",
       "      <th>VAR_1810</th>\n",
       "      <th>VAR_1859</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>FT LAUDERDALE</td>\n",
       "      <td>33324.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48233</td>\n",
       "      <td>73627</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>49463</td>\n",
       "      <td>...</td>\n",
       "      <td>39926</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>49463</td>\n",
       "      <td>49463</td>\n",
       "      <td>30537</td>\n",
       "      <td>30537</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SANTEE</td>\n",
       "      <td>92071.0</td>\n",
       "      <td>163400</td>\n",
       "      <td>163400</td>\n",
       "      <td>2407</td>\n",
       "      <td>3502</td>\n",
       "      <td>26051</td>\n",
       "      <td>2805</td>\n",
       "      <td>2625</td>\n",
       "      <td>798</td>\n",
       "      <td>...</td>\n",
       "      <td>2847</td>\n",
       "      <td>32597</td>\n",
       "      <td>33667</td>\n",
       "      <td>27542</td>\n",
       "      <td>11222</td>\n",
       "      <td>5296</td>\n",
       "      <td>5296</td>\n",
       "      <td>2847</td>\n",
       "      <td>2847</td>\n",
       "      <td>2684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>REEDSVILLE</td>\n",
       "      <td>26547.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>10161</td>\n",
       "      <td>2116</td>\n",
       "      <td>7071</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999997</td>\n",
       "      <td>12277</td>\n",
       "      <td>22523</td>\n",
       "      <td>15452</td>\n",
       "      <td>11262</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LIBERTY</td>\n",
       "      <td>77575.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "      <td>1903</td>\n",
       "      <td>20593</td>\n",
       "      <td>18802</td>\n",
       "      <td>29182</td>\n",
       "      <td>183</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>45508</td>\n",
       "      <td>59190</td>\n",
       "      <td>29182</td>\n",
       "      <td>9865</td>\n",
       "      <td>286</td>\n",
       "      <td>2060</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>FRANKFORT</td>\n",
       "      <td>60423.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6183</td>\n",
       "      <td>6693</td>\n",
       "      <td>10071</td>\n",
       "      <td>10071</td>\n",
       "      <td>20947</td>\n",
       "      <td>1918</td>\n",
       "      <td>...</td>\n",
       "      <td>3357</td>\n",
       "      <td>10071</td>\n",
       "      <td>20947</td>\n",
       "      <td>20947</td>\n",
       "      <td>20947</td>\n",
       "      <td>10404</td>\n",
       "      <td>13576</td>\n",
       "      <td>3357</td>\n",
       "      <td>3357</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>SPRING</td>\n",
       "      <td>77379.0</td>\n",
       "      <td>127200</td>\n",
       "      <td>127200</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>18877</td>\n",
       "      <td>1025</td>\n",
       "      <td>4000</td>\n",
       "      <td>692</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>19902</td>\n",
       "      <td>26755</td>\n",
       "      <td>22755</td>\n",
       "      <td>13378</td>\n",
       "      <td>1237</td>\n",
       "      <td>1237</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>GRESHAM</td>\n",
       "      <td>97030.0</td>\n",
       "      <td>252970</td>\n",
       "      <td>252970</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>5818</td>\n",
       "      <td>5818</td>\n",
       "      <td>23665</td>\n",
       "      <td>116</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>5818</td>\n",
       "      <td>23665</td>\n",
       "      <td>23665</td>\n",
       "      <td>23665</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>999999997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>WARNER ROBINS</td>\n",
       "      <td>31098.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>2961</td>\n",
       "      <td>1015</td>\n",
       "      <td>1544</td>\n",
       "      <td>219</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>3976</td>\n",
       "      <td>4681</td>\n",
       "      <td>3137</td>\n",
       "      <td>2341</td>\n",
       "      <td>350</td>\n",
       "      <td>350</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>1365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SAN ANTONIO</td>\n",
       "      <td>78212.0</td>\n",
       "      <td>68120</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>20359</td>\n",
       "      <td>820</td>\n",
       "      <td>3048</td>\n",
       "      <td>412</td>\n",
       "      <td>...</td>\n",
       "      <td>308</td>\n",
       "      <td>23389</td>\n",
       "      <td>28314</td>\n",
       "      <td>21942</td>\n",
       "      <td>5663</td>\n",
       "      <td>1580</td>\n",
       "      <td>3739</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "      <td>5209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>362</td>\n",
       "      <td>1127</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>118</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1197</td>\n",
       "      <td>1197</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>MURFREESBORO</td>\n",
       "      <td>37129.0</td>\n",
       "      <td>33850</td>\n",
       "      <td>33850</td>\n",
       "      <td>921</td>\n",
       "      <td>921</td>\n",
       "      <td>6088</td>\n",
       "      <td>6088</td>\n",
       "      <td>14233</td>\n",
       "      <td>504</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>6088</td>\n",
       "      <td>14233</td>\n",
       "      <td>14233</td>\n",
       "      <td>14233</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>CARTERSVILLE</td>\n",
       "      <td>30120.0</td>\n",
       "      <td>33080</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>139</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>136</td>\n",
       "      <td>111</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>BETHLEHEM</td>\n",
       "      <td>18018.0</td>\n",
       "      <td>30800</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>383</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>752</td>\n",
       "      <td>1158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>GIG HARBOR</td>\n",
       "      <td>98335.0</td>\n",
       "      <td>813900</td>\n",
       "      <td>813900</td>\n",
       "      <td>3231</td>\n",
       "      <td>3231</td>\n",
       "      <td>14359</td>\n",
       "      <td>14359</td>\n",
       "      <td>44062</td>\n",
       "      <td>1663</td>\n",
       "      <td>...</td>\n",
       "      <td>153</td>\n",
       "      <td>14359</td>\n",
       "      <td>44062</td>\n",
       "      <td>44062</td>\n",
       "      <td>44062</td>\n",
       "      <td>3347</td>\n",
       "      <td>3347</td>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>BAKERSFIELD</td>\n",
       "      <td>93308.0</td>\n",
       "      <td>137497</td>\n",
       "      <td>0</td>\n",
       "      <td>3055</td>\n",
       "      <td>5752</td>\n",
       "      <td>14112</td>\n",
       "      <td>9006</td>\n",
       "      <td>36563</td>\n",
       "      <td>434</td>\n",
       "      <td>...</td>\n",
       "      <td>2462</td>\n",
       "      <td>23372</td>\n",
       "      <td>53915</td>\n",
       "      <td>36563</td>\n",
       "      <td>17972</td>\n",
       "      <td>7864</td>\n",
       "      <td>7864</td>\n",
       "      <td>2462</td>\n",
       "      <td>2462</td>\n",
       "      <td>3909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>HERNDON</td>\n",
       "      <td>20171.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>10040</td>\n",
       "      <td>10040</td>\n",
       "      <td>11119</td>\n",
       "      <td>407</td>\n",
       "      <td>...</td>\n",
       "      <td>641</td>\n",
       "      <td>10040</td>\n",
       "      <td>11119</td>\n",
       "      <td>11119</td>\n",
       "      <td>11119</td>\n",
       "      <td>359</td>\n",
       "      <td>359</td>\n",
       "      <td>641</td>\n",
       "      <td>641</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>SHELBY GAP</td>\n",
       "      <td>41563.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>0</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>4880</td>\n",
       "      <td>4880</td>\n",
       "      <td>5080</td>\n",
       "      <td>545</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>8620</td>\n",
       "      <td>9007</td>\n",
       "      <td>5080</td>\n",
       "      <td>4504</td>\n",
       "      <td>545</td>\n",
       "      <td>545</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>999999996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>ONTARIO</td>\n",
       "      <td>91761.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1061</td>\n",
       "      <td>3597</td>\n",
       "      <td>12900</td>\n",
       "      <td>6691</td>\n",
       "      <td>11615</td>\n",
       "      <td>1487</td>\n",
       "      <td>...</td>\n",
       "      <td>3189</td>\n",
       "      <td>21735</td>\n",
       "      <td>28040</td>\n",
       "      <td>14125</td>\n",
       "      <td>9347</td>\n",
       "      <td>4361</td>\n",
       "      <td>4361</td>\n",
       "      <td>3189</td>\n",
       "      <td>3189</td>\n",
       "      <td>2645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>38134.0</td>\n",
       "      <td>36350</td>\n",
       "      <td>36350</td>\n",
       "      <td>788</td>\n",
       "      <td>6317</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27888</td>\n",
       "      <td>278</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>31089</td>\n",
       "      <td>31680</td>\n",
       "      <td>27888</td>\n",
       "      <td>15840</td>\n",
       "      <td>888</td>\n",
       "      <td>6687</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>5799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>BAKERSFIED</td>\n",
       "      <td>93304.0</td>\n",
       "      <td>46000</td>\n",
       "      <td>46000</td>\n",
       "      <td>3730</td>\n",
       "      <td>4173</td>\n",
       "      <td>13898</td>\n",
       "      <td>13898</td>\n",
       "      <td>18866</td>\n",
       "      <td>2971</td>\n",
       "      <td>...</td>\n",
       "      <td>513</td>\n",
       "      <td>13898</td>\n",
       "      <td>18866</td>\n",
       "      <td>18866</td>\n",
       "      <td>18866</td>\n",
       "      <td>6224</td>\n",
       "      <td>6224</td>\n",
       "      <td>514</td>\n",
       "      <td>514</td>\n",
       "      <td>2244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>ALTAMONTE SPRINGS</td>\n",
       "      <td>32714.0</td>\n",
       "      <td>21536</td>\n",
       "      <td>21536</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>3524</td>\n",
       "      <td>3524</td>\n",
       "      <td>7233</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>521</td>\n",
       "      <td>3524</td>\n",
       "      <td>7233</td>\n",
       "      <td>7233</td>\n",
       "      <td>7233</td>\n",
       "      <td>2314</td>\n",
       "      <td>2314</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>BARNESVILLE</td>\n",
       "      <td>18214.0</td>\n",
       "      <td>57870</td>\n",
       "      <td>57870</td>\n",
       "      <td>8362</td>\n",
       "      <td>11494</td>\n",
       "      <td>129873</td>\n",
       "      <td>129873</td>\n",
       "      <td>143000</td>\n",
       "      <td>4276</td>\n",
       "      <td>...</td>\n",
       "      <td>6503</td>\n",
       "      <td>170922</td>\n",
       "      <td>185442</td>\n",
       "      <td>143000</td>\n",
       "      <td>92721</td>\n",
       "      <td>12127</td>\n",
       "      <td>12127</td>\n",
       "      <td>6503</td>\n",
       "      <td>6503</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>77015.0</td>\n",
       "      <td>85000</td>\n",
       "      <td>85000</td>\n",
       "      <td>481</td>\n",
       "      <td>1699</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999996</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1392</td>\n",
       "      <td>...</td>\n",
       "      <td>5839</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>999999997</td>\n",
       "      <td>5565</td>\n",
       "      <td>5565</td>\n",
       "      <td>5839</td>\n",
       "      <td>5839</td>\n",
       "      <td>1368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>PERU</td>\n",
       "      <td>46970.0</td>\n",
       "      <td>43500</td>\n",
       "      <td>101900</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "      <td>999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>TACOMA</td>\n",
       "      <td>98444.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999996</td>\n",
       "      <td>1270</td>\n",
       "      <td>204</td>\n",
       "      <td>2248</td>\n",
       "      <td>949</td>\n",
       "      <td>...</td>\n",
       "      <td>334</td>\n",
       "      <td>1474</td>\n",
       "      <td>3605</td>\n",
       "      <td>2248</td>\n",
       "      <td>1803</td>\n",
       "      <td>1016</td>\n",
       "      <td>1016</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>OZARK</td>\n",
       "      <td>65721.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2458</td>\n",
       "      <td>-99999</td>\n",
       "      <td>...</td>\n",
       "      <td>999999998</td>\n",
       "      <td>2015</td>\n",
       "      <td>2458</td>\n",
       "      <td>2458</td>\n",
       "      <td>2458</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "      <td>999999998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             VAR_0200  VAR_0241 VAR_0293 VAR_0313   VAR_0585   VAR_0609  \\\n",
       "0       FT LAUDERDALE   33324.0        0        0      48233      73627   \n",
       "1              SANTEE   92071.0   163400   163400       2407       3502   \n",
       "2          REEDSVILLE   26547.0        0        0  999999997  999999997   \n",
       "3             LIBERTY   77575.0        0        0        236       1903   \n",
       "4           FRANKFORT   60423.0        0        0       6183       6693   \n",
       "5              SPRING   77379.0   127200   127200        412        412   \n",
       "6             GRESHAM   97030.0   252970   252970  999999996  999999996   \n",
       "7       WARNER ROBINS   31098.0        0        0  999999996  999999996   \n",
       "8         SAN ANTONIO   78212.0    68120        0  999999996  999999996   \n",
       "9          NORRISTOWN   19401.0        0        0        362       1127   \n",
       "10       MURFREESBORO   37129.0    33850    33850        921        921   \n",
       "11       CARTERSVILLE   30120.0    33080        0  999999996        139   \n",
       "12          BETHLEHEM   18018.0    30800        0  999999996  999999996   \n",
       "13         GIG HARBOR   98335.0   813900   813900       3231       3231   \n",
       "14        BAKERSFIELD   93308.0   137497        0       3055       5752   \n",
       "15            HERNDON   20171.0        0        0  999999996  999999996   \n",
       "16         SHELBY GAP   41563.0    35000        0  999999996  999999996   \n",
       "17            ONTARIO   91761.0        0        0       1061       3597   \n",
       "18            MEMPHIS   38134.0    36350    36350        788       6317   \n",
       "19         BAKERSFIED   93304.0    46000    46000       3730       4173   \n",
       "20  ALTAMONTE SPRINGS   32714.0    21536    21536        179        179   \n",
       "21        BARNESVILLE   18214.0    57870    57870       8362      11494   \n",
       "22            HOUSTON   77015.0    85000    85000        481       1699   \n",
       "23               PERU   46970.0    43500   101900  999999999  999999999   \n",
       "24             TACOMA   98444.0        0        0  999999998  999999996   \n",
       "25              OZARK   65721.0        0        0  999999998  999999998   \n",
       "\n",
       "     VAR_0648   VAR_0649   VAR_0652  VAR_0705  ...   VAR_1374   VAR_1489  \\\n",
       "0   999999997  999999996  999999996     49463  ...      39926  999999997   \n",
       "1       26051       2805       2625       798  ...       2847      32597   \n",
       "2       10161       2116       7071    -99999  ...  999999997      12277   \n",
       "3       20593      18802      29182       183  ...         14      45508   \n",
       "4       10071      10071      20947      1918  ...       3357      10071   \n",
       "5       18877       1025       4000       692  ...         88      19902   \n",
       "6        5818       5818      23665       116  ...        157       5818   \n",
       "7        2961       1015       1544       219  ...        150       3976   \n",
       "8       20359        820       3048       412  ...        308      23389   \n",
       "9   999999996  999999996  999999996    -99999  ...        118  999999996   \n",
       "10       6088       6088      14233       504  ...         11       6088   \n",
       "11  999999997  999999996  999999996    -99999  ...        136  999999997   \n",
       "12  999999997  999999996  999999996       383  ...          0  999999997   \n",
       "13      14359      14359      44062      1663  ...        153      14359   \n",
       "14      14112       9006      36563       434  ...       2462      23372   \n",
       "15      10040      10040      11119       407  ...        641      10040   \n",
       "16       4880       4880       5080       545  ...         55       8620   \n",
       "17      12900       6691      11615      1487  ...       3189      21735   \n",
       "18      27457      27457      27888       278  ...         29      31089   \n",
       "19      13898      13898      18866      2971  ...        513      13898   \n",
       "20       3524       3524       7233       244  ...        521       3524   \n",
       "21     129873     129873     143000      4276  ...       6503     170922   \n",
       "22  999999997  999999996  999999996      1392  ...       5839  999999997   \n",
       "23  999999999  999999999  999999999    -99999  ...  999999999  999999999   \n",
       "24       1270        204       2248       949  ...        334       1474   \n",
       "25       2015       2015       2458    -99999  ...  999999998       2015   \n",
       "\n",
       "     VAR_1494   VAR_1496   VAR_1497   VAR_1801   VAR_1802   VAR_1809  \\\n",
       "0   999999997  999999997  999999997      49463      49463      30537   \n",
       "1       33667      27542      11222       5296       5296       2847   \n",
       "2       22523      15452      11262  999999997  999999997  999999997   \n",
       "3       59190      29182       9865        286       2060         14   \n",
       "4       20947      20947      20947      10404      13576       3357   \n",
       "5       26755      22755      13378       1237       1237         88   \n",
       "6       23665      23665      23665        143        143        157   \n",
       "7        4681       3137       2341        350        350        150   \n",
       "8       28314      21942       5663       1580       3739        308   \n",
       "9   999999996  999999996  999999996       1197       1197        118   \n",
       "10      14233      14233      14233        989        989         11   \n",
       "11  999999997  999999997  999999997        621        621        136   \n",
       "12  999999997  999999997  999999997        752       1158          0   \n",
       "13      44062      44062      44062       3347       3347        153   \n",
       "14      53915      36563      17972       7864       7864       2462   \n",
       "15      11119      11119      11119        359        359        641   \n",
       "16       9007       5080       4504        545        545         55   \n",
       "17      28040      14125       9347       4361       4361       3189   \n",
       "18      31680      27888      15840        888       6687         29   \n",
       "19      18866      18866      18866       6224       6224        514   \n",
       "20       7233       7233       7233       2314       2314        521   \n",
       "21     185442     143000      92721      12127      12127       6503   \n",
       "22  999999997  999999997  999999997       5565       5565       5839   \n",
       "23  999999999  999999999  999999999  999999999  999999999  999999999   \n",
       "24       3605       2248       1803       1016       1016        334   \n",
       "25       2458       2458       2458  999999998  999999998  999999998   \n",
       "\n",
       "     VAR_1810   VAR_1859  \n",
       "0       30537  999999997  \n",
       "1        2847       2684  \n",
       "2   999999997  999999997  \n",
       "3          14       1774  \n",
       "4        3357        568  \n",
       "5          88  999999998  \n",
       "6         157  999999997  \n",
       "7         150       1365  \n",
       "8         308       5209  \n",
       "9         118        815  \n",
       "10         11  999999998  \n",
       "11        111        621  \n",
       "12          0        369  \n",
       "13        153          0  \n",
       "14       2462       3909  \n",
       "15        641        359  \n",
       "16         55  999999996  \n",
       "17       3189       2645  \n",
       "18         29       5799  \n",
       "19        514       2244  \n",
       "20        521          0  \n",
       "21       6503       3530  \n",
       "22       5839       1368  \n",
       "23  999999999  999999999  \n",
       "24        334       1016  \n",
       "25  999999998  999999998  \n",
       "\n",
       "[26 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "# Our conclusion: there are no floating point variables, there are some counts variables, which we will treat as numeric. \n",
    "# And finally, let's pick one variable (in this case 'VAR_0015') from the third group of features.\n",
    "\n",
    "X_train['VAR_0015'].value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0     3595\n",
       "1.0      936\n",
       "2.0      294\n",
       "3.0       94\n",
       "4.0       38\n",
       "5.0       21\n",
       "6.0       12\n",
       "7.0        4\n",
       "8.0        2\n",
       "10.0       2\n",
       "14.0       1\n",
       "9.0        1\n",
       "Name: VAR_0015, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "# 把分类型和数值型的特征分别跳出来\n",
    "cat_cols = list(X_train.select_dtypes(include=['object']).columns)\n",
    "num_cols = list(X_train.select_dtypes(exclude=['object']).columns)\n",
    "print(cat_cols)\n",
    "print(num_cols)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['VAR_0001', 'VAR_0005', 'VAR_0044', 'VAR_0073', 'VAR_0074', 'VAR_0075', 'VAR_0156', 'VAR_0157', 'VAR_0158', 'VAR_0159', 'VAR_0166', 'VAR_0167', 'VAR_0168', 'VAR_0169', 'VAR_0176', 'VAR_0177', 'VAR_0178', 'VAR_0179', 'VAR_0200', 'VAR_0202', 'VAR_0204', 'VAR_0205', 'VAR_0206', 'VAR_0207', 'VAR_0208', 'VAR_0209', 'VAR_0210', 'VAR_0211', 'VAR_0212', 'VAR_0213', 'VAR_0214', 'VAR_0216', 'VAR_0217', 'VAR_0222', 'VAR_0237', 'VAR_0242', 'VAR_0243', 'VAR_0244', 'VAR_0245', 'VAR_0246', 'VAR_0247', 'VAR_0248', 'VAR_0249', 'VAR_0250', 'VAR_0251', 'VAR_0252', 'VAR_0253', 'VAR_0254', 'VAR_0255', 'VAR_0256', 'VAR_0257', 'VAR_0258', 'VAR_0259', 'VAR_0260', 'VAR_0261', 'VAR_0262', 'VAR_0263', 'VAR_0264', 'VAR_0265', 'VAR_0266', 'VAR_0267', 'VAR_0268', 'VAR_0269', 'VAR_0270', 'VAR_0271', 'VAR_0272', 'VAR_0273', 'VAR_0274', 'VAR_0275', 'VAR_0276', 'VAR_0277', 'VAR_0278', 'VAR_0279', 'VAR_0280', 'VAR_0281', 'VAR_0282', 'VAR_0283', 'VAR_0284', 'VAR_0285', 'VAR_0286', 'VAR_0287', 'VAR_0288', 'VAR_0289', 'VAR_0290', 'VAR_0291', 'VAR_0292', 'VAR_0293', 'VAR_0294', 'VAR_0295', 'VAR_0296', 'VAR_0297', 'VAR_0298', 'VAR_0299', 'VAR_0300', 'VAR_0301', 'VAR_0302', 'VAR_0303', 'VAR_0304', 'VAR_0305', 'VAR_0306', 'VAR_0307', 'VAR_0308', 'VAR_0309', 'VAR_0310', 'VAR_0311', 'VAR_0312', 'VAR_0313', 'VAR_0314', 'VAR_0315', 'VAR_0316', 'VAR_0317', 'VAR_0318', 'VAR_0319', 'VAR_0320', 'VAR_0321', 'VAR_0322', 'VAR_0323', 'VAR_0324', 'VAR_0325', 'VAR_0326', 'VAR_0327', 'VAR_0328', 'VAR_0329', 'VAR_0330', 'VAR_0331', 'VAR_0332', 'VAR_0333', 'VAR_0334', 'VAR_0335', 'VAR_0336', 'VAR_0337', 'VAR_0338', 'VAR_0339', 'VAR_0340', 'VAR_0341', 'VAR_0342', 'VAR_0343', 'VAR_0344', 'VAR_0345', 'VAR_0346', 'VAR_0347', 'VAR_0348', 'VAR_0349', 'VAR_0350', 'VAR_0351', 'VAR_0352', 'VAR_0353', 'VAR_0354', 'VAR_0355', 'VAR_0356', 'VAR_0357', 'VAR_0358', 'VAR_0359', 'VAR_0360', 'VAR_0361', 'VAR_0362', 'VAR_0363', 'VAR_0364', 'VAR_0365', 'VAR_0366', 'VAR_0367', 'VAR_0368', 'VAR_0369', 'VAR_0370', 'VAR_0371', 'VAR_0372', 'VAR_0373', 'VAR_0374', 'VAR_0375', 'VAR_0376', 'VAR_0377', 'VAR_0378', 'VAR_0379', 'VAR_0380', 'VAR_0381', 'VAR_0382', 'VAR_0383', 'VAR_0384', 'VAR_0385', 'VAR_0386', 'VAR_0387', 'VAR_0388', 'VAR_0389', 'VAR_0390', 'VAR_0391', 'VAR_0392', 'VAR_0393', 'VAR_0394', 'VAR_0395', 'VAR_0396', 'VAR_0397', 'VAR_0398', 'VAR_0399', 'VAR_0400', 'VAR_0401', 'VAR_0402', 'VAR_0403', 'VAR_0404', 'VAR_0405', 'VAR_0406', 'VAR_0407', 'VAR_0408', 'VAR_0409', 'VAR_0410', 'VAR_0411', 'VAR_0412', 'VAR_0413', 'VAR_0414', 'VAR_0415', 'VAR_0416', 'VAR_0417', 'VAR_0418', 'VAR_0419', 'VAR_0420', 'VAR_0421', 'VAR_0422', 'VAR_0423', 'VAR_0424', 'VAR_0425', 'VAR_0426', 'VAR_0427', 'VAR_0428', 'VAR_0429', 'VAR_0430', 'VAR_0431', 'VAR_0432', 'VAR_0433', 'VAR_0434', 'VAR_0435', 'VAR_0436', 'VAR_0437', 'VAR_0438', 'VAR_0439', 'VAR_0440', 'VAR_0441', 'VAR_0442', 'VAR_0443', 'VAR_0444', 'VAR_0445', 'VAR_0446', 'VAR_0447', 'VAR_0448', 'VAR_0449', 'VAR_0450', 'VAR_0451', 'VAR_0452', 'VAR_0453', 'VAR_0454', 'VAR_0455', 'VAR_0456', 'VAR_0457', 'VAR_0458', 'VAR_0459', 'VAR_0460', 'VAR_0461', 'VAR_0462', 'VAR_0463', 'VAR_0464', 'VAR_0465', 'VAR_0466', 'VAR_0467', 'VAR_0468', 'VAR_0469', 'VAR_0470', 'VAR_0471', 'VAR_0472', 'VAR_0473', 'VAR_0474', 'VAR_0475', 'VAR_0476', 'VAR_0477', 'VAR_0478', 'VAR_0479', 'VAR_0480', 'VAR_0481', 'VAR_0482', 'VAR_0483', 'VAR_0484', 'VAR_0485', 'VAR_0486', 'VAR_0487', 'VAR_0488', 'VAR_0489', 'VAR_0490', 'VAR_0491', 'VAR_0492', 'VAR_0493', 'VAR_0494', 'VAR_0495', 'VAR_0496', 'VAR_0497', 'VAR_0498', 'VAR_0499', 'VAR_0500', 'VAR_0501', 'VAR_0502', 'VAR_0503', 'VAR_0504', 'VAR_0505', 'VAR_0506', 'VAR_0507', 'VAR_0508', 'VAR_0509', 'VAR_0510', 'VAR_0511', 'VAR_0512', 'VAR_0513', 'VAR_0514', 'VAR_0515', 'VAR_0516', 'VAR_0517', 'VAR_0518', 'VAR_0519', 'VAR_0520', 'VAR_0521', 'VAR_0522', 'VAR_0523', 'VAR_0524', 'VAR_0525', 'VAR_0526', 'VAR_0527', 'VAR_0528', 'VAR_0529', 'VAR_0530', 'VAR_0531', 'VAR_0840', 'VAR_1934']\n",
      "['ID', 'VAR_0002', 'VAR_0003', 'VAR_0004', 'VAR_0006', 'VAR_0007', 'VAR_0008', 'VAR_0009', 'VAR_0010', 'VAR_0011', 'VAR_0012', 'VAR_0013', 'VAR_0014', 'VAR_0015', 'VAR_0016', 'VAR_0017', 'VAR_0018', 'VAR_0019', 'VAR_0020', 'VAR_0021', 'VAR_0022', 'VAR_0023', 'VAR_0024', 'VAR_0025', 'VAR_0026', 'VAR_0027', 'VAR_0028', 'VAR_0029', 'VAR_0030', 'VAR_0031', 'VAR_0032', 'VAR_0033', 'VAR_0034', 'VAR_0035', 'VAR_0036', 'VAR_0037', 'VAR_0038', 'VAR_0039', 'VAR_0040', 'VAR_0041', 'VAR_0042', 'VAR_0043', 'VAR_0045', 'VAR_0046', 'VAR_0047', 'VAR_0048', 'VAR_0049', 'VAR_0050', 'VAR_0051', 'VAR_0052', 'VAR_0053', 'VAR_0054', 'VAR_0055', 'VAR_0056', 'VAR_0057', 'VAR_0058', 'VAR_0059', 'VAR_0060', 'VAR_0061', 'VAR_0062', 'VAR_0063', 'VAR_0064', 'VAR_0065', 'VAR_0066', 'VAR_0067', 'VAR_0068', 'VAR_0069', 'VAR_0070', 'VAR_0071', 'VAR_0072', 'VAR_0076', 'VAR_0077', 'VAR_0078', 'VAR_0079', 'VAR_0080', 'VAR_0081', 'VAR_0082', 'VAR_0083', 'VAR_0084', 'VAR_0085', 'VAR_0086', 'VAR_0087', 'VAR_0088', 'VAR_0089', 'VAR_0090', 'VAR_0091', 'VAR_0092', 'VAR_0093', 'VAR_0094', 'VAR_0095', 'VAR_0096', 'VAR_0097', 'VAR_0098', 'VAR_0099', 'VAR_0100', 'VAR_0101', 'VAR_0102', 'VAR_0103', 'VAR_0104', 'VAR_0105', 'VAR_0106', 'VAR_0107', 'VAR_0108', 'VAR_0109', 'VAR_0110', 'VAR_0111', 'VAR_0112', 'VAR_0113', 'VAR_0114', 'VAR_0115', 'VAR_0116', 'VAR_0117', 'VAR_0118', 'VAR_0119', 'VAR_0120', 'VAR_0121', 'VAR_0122', 'VAR_0123', 'VAR_0124', 'VAR_0125', 'VAR_0126', 'VAR_0127', 'VAR_0128', 'VAR_0129', 'VAR_0130', 'VAR_0131', 'VAR_0132', 'VAR_0133', 'VAR_0134', 'VAR_0135', 'VAR_0136', 'VAR_0137', 'VAR_0138', 'VAR_0139', 'VAR_0140', 'VAR_0141', 'VAR_0142', 'VAR_0143', 'VAR_0144', 'VAR_0145', 'VAR_0146', 'VAR_0147', 'VAR_0148', 'VAR_0149', 'VAR_0150', 'VAR_0151', 'VAR_0152', 'VAR_0153', 'VAR_0154', 'VAR_0155', 'VAR_0160', 'VAR_0161', 'VAR_0162', 'VAR_0163', 'VAR_0164', 'VAR_0165', 'VAR_0170', 'VAR_0171', 'VAR_0172', 'VAR_0173', 'VAR_0174', 'VAR_0175', 'VAR_0180', 'VAR_0181', 'VAR_0182', 'VAR_0183', 'VAR_0184', 'VAR_0185', 'VAR_0186', 'VAR_0187', 'VAR_0188', 'VAR_0189', 'VAR_0190', 'VAR_0191', 'VAR_0192', 'VAR_0193', 'VAR_0194', 'VAR_0195', 'VAR_0196', 'VAR_0197', 'VAR_0198', 'VAR_0199', 'VAR_0201', 'VAR_0203', 'VAR_0215', 'VAR_0219', 'VAR_0220', 'VAR_0221', 'VAR_0223', 'VAR_0224', 'VAR_0225', 'VAR_0226', 'VAR_0227', 'VAR_0228', 'VAR_0229', 'VAR_0230', 'VAR_0231', 'VAR_0232', 'VAR_0233', 'VAR_0234', 'VAR_0235', 'VAR_0236', 'VAR_0238', 'VAR_0239', 'VAR_0241', 'VAR_0532', 'VAR_0533', 'VAR_0534', 'VAR_0535', 'VAR_0536', 'VAR_0537', 'VAR_0538', 'VAR_0539', 'VAR_0540', 'VAR_0541', 'VAR_0542', 'VAR_0543', 'VAR_0544', 'VAR_0545', 'VAR_0546', 'VAR_0547', 'VAR_0548', 'VAR_0549', 'VAR_0550', 'VAR_0551', 'VAR_0552', 'VAR_0553', 'VAR_0554', 'VAR_0555', 'VAR_0556', 'VAR_0557', 'VAR_0558', 'VAR_0559', 'VAR_0560', 'VAR_0561', 'VAR_0562', 'VAR_0563', 'VAR_0564', 'VAR_0565', 'VAR_0566', 'VAR_0567', 'VAR_0568', 'VAR_0569', 'VAR_0570', 'VAR_0571', 'VAR_0572', 'VAR_0573', 'VAR_0574', 'VAR_0575', 'VAR_0576', 'VAR_0577', 'VAR_0578', 'VAR_0579', 'VAR_0580', 'VAR_0581', 'VAR_0582', 'VAR_0583', 'VAR_0584', 'VAR_0585', 'VAR_0586', 'VAR_0587', 'VAR_0588', 'VAR_0589', 'VAR_0590', 'VAR_0591', 'VAR_0592', 'VAR_0593', 'VAR_0594', 'VAR_0595', 'VAR_0596', 'VAR_0597', 'VAR_0598', 'VAR_0599', 'VAR_0600', 'VAR_0601', 'VAR_0602', 'VAR_0603', 'VAR_0604', 'VAR_0605', 'VAR_0606', 'VAR_0607', 'VAR_0608', 'VAR_0609', 'VAR_0610', 'VAR_0611', 'VAR_0612', 'VAR_0613', 'VAR_0614', 'VAR_0615', 'VAR_0616', 'VAR_0617', 'VAR_0618', 'VAR_0619', 'VAR_0620', 'VAR_0621', 'VAR_0622', 'VAR_0623', 'VAR_0624', 'VAR_0625', 'VAR_0626', 'VAR_0627', 'VAR_0628', 'VAR_0629', 'VAR_0630', 'VAR_0631', 'VAR_0632', 'VAR_0633', 'VAR_0634', 'VAR_0635', 'VAR_0636', 'VAR_0637', 'VAR_0638', 'VAR_0639', 'VAR_0640', 'VAR_0641', 'VAR_0642', 'VAR_0643', 'VAR_0644', 'VAR_0645', 'VAR_0646', 'VAR_0647', 'VAR_0648', 'VAR_0649', 'VAR_0650', 'VAR_0651', 'VAR_0652', 'VAR_0653', 'VAR_0654', 'VAR_0655', 'VAR_0656', 'VAR_0657', 'VAR_0658', 'VAR_0659', 'VAR_0660', 'VAR_0661', 'VAR_0662', 'VAR_0663', 'VAR_0664', 'VAR_0665', 'VAR_0666', 'VAR_0667', 'VAR_0668', 'VAR_0669', 'VAR_0670', 'VAR_0671', 'VAR_0672', 'VAR_0673', 'VAR_0674', 'VAR_0675', 'VAR_0676', 'VAR_0677', 'VAR_0678', 'VAR_0679', 'VAR_0680', 'VAR_0681', 'VAR_0682', 'VAR_0683', 'VAR_0684', 'VAR_0685', 'VAR_0686', 'VAR_0687', 'VAR_0688', 'VAR_0689', 'VAR_0690', 'VAR_0691', 'VAR_0692', 'VAR_0693', 'VAR_0694', 'VAR_0695', 'VAR_0696', 'VAR_0697', 'VAR_0698', 'VAR_0699', 'VAR_0700', 'VAR_0701', 'VAR_0702', 'VAR_0703', 'VAR_0704', 'VAR_0705', 'VAR_0706', 'VAR_0707', 'VAR_0708', 'VAR_0709', 'VAR_0710', 'VAR_0711', 'VAR_0712', 'VAR_0713', 'VAR_0714', 'VAR_0715', 'VAR_0716', 'VAR_0717', 'VAR_0718', 'VAR_0719', 'VAR_0720', 'VAR_0721', 'VAR_0722', 'VAR_0723', 'VAR_0724', 'VAR_0725', 'VAR_0726', 'VAR_0727', 'VAR_0728', 'VAR_0729', 'VAR_0730', 'VAR_0731', 'VAR_0732', 'VAR_0733', 'VAR_0734', 'VAR_0735', 'VAR_0736', 'VAR_0737', 'VAR_0738', 'VAR_0739', 'VAR_0740', 'VAR_0741', 'VAR_0742', 'VAR_0743', 'VAR_0744', 'VAR_0745', 'VAR_0746', 'VAR_0747', 'VAR_0748', 'VAR_0749', 'VAR_0750', 'VAR_0751', 'VAR_0752', 'VAR_0753', 'VAR_0754', 'VAR_0755', 'VAR_0756', 'VAR_0757', 'VAR_0758', 'VAR_0759', 'VAR_0760', 'VAR_0761', 'VAR_0762', 'VAR_0763', 'VAR_0764', 'VAR_0765', 'VAR_0766', 'VAR_0767', 'VAR_0768', 'VAR_0769', 'VAR_0770', 'VAR_0771', 'VAR_0772', 'VAR_0773', 'VAR_0774', 'VAR_0775', 'VAR_0776', 'VAR_0777', 'VAR_0778', 'VAR_0779', 'VAR_0780', 'VAR_0781', 'VAR_0782', 'VAR_0783', 'VAR_0784', 'VAR_0785', 'VAR_0786', 'VAR_0787', 'VAR_0788', 'VAR_0789', 'VAR_0790', 'VAR_0791', 'VAR_0792', 'VAR_0793', 'VAR_0794', 'VAR_0795', 'VAR_0796', 'VAR_0797', 'VAR_0798', 'VAR_0799', 'VAR_0800', 'VAR_0801', 'VAR_0802', 'VAR_0803', 'VAR_0804', 'VAR_0805', 'VAR_0806', 'VAR_0807', 'VAR_0808', 'VAR_0809', 'VAR_0810', 'VAR_0811', 'VAR_0812', 'VAR_0813', 'VAR_0814', 'VAR_0815', 'VAR_0816', 'VAR_0817', 'VAR_0818', 'VAR_0819', 'VAR_0820', 'VAR_0821', 'VAR_0822', 'VAR_0823', 'VAR_0824', 'VAR_0825', 'VAR_0826', 'VAR_0827', 'VAR_0828', 'VAR_0829', 'VAR_0830', 'VAR_0831', 'VAR_0832', 'VAR_0833', 'VAR_0834', 'VAR_0835', 'VAR_0836', 'VAR_0837', 'VAR_0838', 'VAR_0839', 'VAR_0841', 'VAR_0842', 'VAR_0843', 'VAR_0844', 'VAR_0845', 'VAR_0846', 'VAR_0847', 'VAR_0848', 'VAR_0849', 'VAR_0850', 'VAR_0851', 'VAR_0852', 'VAR_0853', 'VAR_0854', 'VAR_0855', 'VAR_0856', 'VAR_0857', 'VAR_0858', 'VAR_0859', 'VAR_0860', 'VAR_0861', 'VAR_0862', 'VAR_0863', 'VAR_0864', 'VAR_0865', 'VAR_0866', 'VAR_0867', 'VAR_0868', 'VAR_0869', 'VAR_0870', 'VAR_0871', 'VAR_0872', 'VAR_0873', 'VAR_0874', 'VAR_0875', 'VAR_0876', 'VAR_0877', 'VAR_0878', 'VAR_0879', 'VAR_0880', 'VAR_0881', 'VAR_0882', 'VAR_0883', 'VAR_0884', 'VAR_0885', 'VAR_0886', 'VAR_0887', 'VAR_0888', 'VAR_0889', 'VAR_0890', 'VAR_0891', 'VAR_0892', 'VAR_0893', 'VAR_0894', 'VAR_0895', 'VAR_0896', 'VAR_0897', 'VAR_0898', 'VAR_0899', 'VAR_0900', 'VAR_0901', 'VAR_0902', 'VAR_0903', 'VAR_0904', 'VAR_0905', 'VAR_0906', 'VAR_0907', 'VAR_0908', 'VAR_0909', 'VAR_0910', 'VAR_0911', 'VAR_0912', 'VAR_0913', 'VAR_0914', 'VAR_0915', 'VAR_0916', 'VAR_0917', 'VAR_0918', 'VAR_0919', 'VAR_0920', 'VAR_0921', 'VAR_0922', 'VAR_0923', 'VAR_0924', 'VAR_0925', 'VAR_0926', 'VAR_0927', 'VAR_0928', 'VAR_0929', 'VAR_0930', 'VAR_0931', 'VAR_0932', 'VAR_0933', 'VAR_0934', 'VAR_0935', 'VAR_0936', 'VAR_0937', 'VAR_0938', 'VAR_0939', 'VAR_0940', 'VAR_0941', 'VAR_0942', 'VAR_0943', 'VAR_0944', 'VAR_0945', 'VAR_0946', 'VAR_0947', 'VAR_0948', 'VAR_0949', 'VAR_0950', 'VAR_0951', 'VAR_0952', 'VAR_0953', 'VAR_0954', 'VAR_0955', 'VAR_0956', 'VAR_0957', 'VAR_0958', 'VAR_0959', 'VAR_0960', 'VAR_0961', 'VAR_0962', 'VAR_0963', 'VAR_0964', 'VAR_0965', 'VAR_0966', 'VAR_0967', 'VAR_0968', 'VAR_0969', 'VAR_0970', 'VAR_0971', 'VAR_0972', 'VAR_0973', 'VAR_0974', 'VAR_0975', 'VAR_0976', 'VAR_0977', 'VAR_0978', 'VAR_0979', 'VAR_0980', 'VAR_0981', 'VAR_0982', 'VAR_0983', 'VAR_0984', 'VAR_0985', 'VAR_0986', 'VAR_0987', 'VAR_0988', 'VAR_0989', 'VAR_0990', 'VAR_0991', 'VAR_0992', 'VAR_0993', 'VAR_0994', 'VAR_0995', 'VAR_0996', 'VAR_0997', 'VAR_0998', 'VAR_0999', 'VAR_1000', 'VAR_1001', 'VAR_1002', 'VAR_1003', 'VAR_1004', 'VAR_1005', 'VAR_1006', 'VAR_1007', 'VAR_1008', 'VAR_1009', 'VAR_1010', 'VAR_1011', 'VAR_1012', 'VAR_1013', 'VAR_1014', 'VAR_1015', 'VAR_1016', 'VAR_1017', 'VAR_1018', 'VAR_1019', 'VAR_1020', 'VAR_1021', 'VAR_1022', 'VAR_1023', 'VAR_1024', 'VAR_1025', 'VAR_1026', 'VAR_1027', 'VAR_1028', 'VAR_1029', 'VAR_1030', 'VAR_1031', 'VAR_1032', 'VAR_1033', 'VAR_1034', 'VAR_1035', 'VAR_1036', 'VAR_1037', 'VAR_1038', 'VAR_1039', 'VAR_1040', 'VAR_1041', 'VAR_1042', 'VAR_1043', 'VAR_1044', 'VAR_1045', 'VAR_1046', 'VAR_1047', 'VAR_1048', 'VAR_1049', 'VAR_1050', 'VAR_1051', 'VAR_1052', 'VAR_1053', 'VAR_1054', 'VAR_1055', 'VAR_1056', 'VAR_1057', 'VAR_1058', 'VAR_1059', 'VAR_1060', 'VAR_1061', 'VAR_1062', 'VAR_1063', 'VAR_1064', 'VAR_1065', 'VAR_1066', 'VAR_1067', 'VAR_1068', 'VAR_1069', 'VAR_1070', 'VAR_1071', 'VAR_1072', 'VAR_1073', 'VAR_1074', 'VAR_1075', 'VAR_1076', 'VAR_1077', 'VAR_1078', 'VAR_1079', 'VAR_1080', 'VAR_1081', 'VAR_1082', 'VAR_1083', 'VAR_1084', 'VAR_1085', 'VAR_1086', 'VAR_1087', 'VAR_1088', 'VAR_1089', 'VAR_1090', 'VAR_1091', 'VAR_1092', 'VAR_1093', 'VAR_1094', 'VAR_1095', 'VAR_1096', 'VAR_1097', 'VAR_1098', 'VAR_1099', 'VAR_1100', 'VAR_1101', 'VAR_1102', 'VAR_1103', 'VAR_1104', 'VAR_1105', 'VAR_1106', 'VAR_1107', 'VAR_1108', 'VAR_1109', 'VAR_1110', 'VAR_1111', 'VAR_1112', 'VAR_1113', 'VAR_1114', 'VAR_1115', 'VAR_1116', 'VAR_1117', 'VAR_1118', 'VAR_1119', 'VAR_1120', 'VAR_1121', 'VAR_1122', 'VAR_1123', 'VAR_1124', 'VAR_1125', 'VAR_1126', 'VAR_1127', 'VAR_1128', 'VAR_1129', 'VAR_1130', 'VAR_1131', 'VAR_1132', 'VAR_1133', 'VAR_1134', 'VAR_1135', 'VAR_1136', 'VAR_1137', 'VAR_1138', 'VAR_1139', 'VAR_1140', 'VAR_1141', 'VAR_1142', 'VAR_1143', 'VAR_1144', 'VAR_1145', 'VAR_1146', 'VAR_1147', 'VAR_1148', 'VAR_1149', 'VAR_1150', 'VAR_1151', 'VAR_1152', 'VAR_1153', 'VAR_1154', 'VAR_1155', 'VAR_1156', 'VAR_1157', 'VAR_1158', 'VAR_1159', 'VAR_1160', 'VAR_1161', 'VAR_1162', 'VAR_1163', 'VAR_1164', 'VAR_1165', 'VAR_1166', 'VAR_1167', 'VAR_1168', 'VAR_1169', 'VAR_1170', 'VAR_1171', 'VAR_1172', 'VAR_1173', 'VAR_1174', 'VAR_1175', 'VAR_1176', 'VAR_1177', 'VAR_1178', 'VAR_1179', 'VAR_1180', 'VAR_1181', 'VAR_1182', 'VAR_1183', 'VAR_1184', 'VAR_1185', 'VAR_1186', 'VAR_1187', 'VAR_1188', 'VAR_1189', 'VAR_1190', 'VAR_1191', 'VAR_1192', 'VAR_1193', 'VAR_1194', 'VAR_1195', 'VAR_1196', 'VAR_1197', 'VAR_1198', 'VAR_1199', 'VAR_1200', 'VAR_1201', 'VAR_1202', 'VAR_1203', 'VAR_1204', 'VAR_1205', 'VAR_1206', 'VAR_1207', 'VAR_1208', 'VAR_1209', 'VAR_1210', 'VAR_1211', 'VAR_1212', 'VAR_1213', 'VAR_1214', 'VAR_1215', 'VAR_1216', 'VAR_1217', 'VAR_1218', 'VAR_1219', 'VAR_1220', 'VAR_1221', 'VAR_1222', 'VAR_1223', 'VAR_1224', 'VAR_1225', 'VAR_1226', 'VAR_1227', 'VAR_1228', 'VAR_1229', 'VAR_1230', 'VAR_1231', 'VAR_1232', 'VAR_1233', 'VAR_1234', 'VAR_1235', 'VAR_1236', 'VAR_1237', 'VAR_1238', 'VAR_1239', 'VAR_1240', 'VAR_1241', 'VAR_1242', 'VAR_1243', 'VAR_1244', 'VAR_1245', 'VAR_1246', 'VAR_1247', 'VAR_1248', 'VAR_1249', 'VAR_1250', 'VAR_1251', 'VAR_1252', 'VAR_1253', 'VAR_1254', 'VAR_1255', 'VAR_1256', 'VAR_1257', 'VAR_1258', 'VAR_1259', 'VAR_1260', 'VAR_1261', 'VAR_1262', 'VAR_1263', 'VAR_1264', 'VAR_1265', 'VAR_1266', 'VAR_1267', 'VAR_1268', 'VAR_1269', 'VAR_1270', 'VAR_1271', 'VAR_1272', 'VAR_1273', 'VAR_1274', 'VAR_1275', 'VAR_1276', 'VAR_1277', 'VAR_1278', 'VAR_1279', 'VAR_1280', 'VAR_1281', 'VAR_1282', 'VAR_1283', 'VAR_1284', 'VAR_1285', 'VAR_1286', 'VAR_1287', 'VAR_1288', 'VAR_1289', 'VAR_1290', 'VAR_1291', 'VAR_1292', 'VAR_1293', 'VAR_1294', 'VAR_1295', 'VAR_1296', 'VAR_1297', 'VAR_1298', 'VAR_1299', 'VAR_1300', 'VAR_1301', 'VAR_1302', 'VAR_1303', 'VAR_1304', 'VAR_1305', 'VAR_1306', 'VAR_1307', 'VAR_1308', 'VAR_1309', 'VAR_1310', 'VAR_1311', 'VAR_1312', 'VAR_1313', 'VAR_1314', 'VAR_1315', 'VAR_1316', 'VAR_1317', 'VAR_1318', 'VAR_1319', 'VAR_1320', 'VAR_1321', 'VAR_1322', 'VAR_1323', 'VAR_1324', 'VAR_1325', 'VAR_1326', 'VAR_1327', 'VAR_1328', 'VAR_1329', 'VAR_1330', 'VAR_1331', 'VAR_1332', 'VAR_1333', 'VAR_1334', 'VAR_1335', 'VAR_1336', 'VAR_1337', 'VAR_1338', 'VAR_1339', 'VAR_1340', 'VAR_1341', 'VAR_1342', 'VAR_1343', 'VAR_1344', 'VAR_1345', 'VAR_1346', 'VAR_1347', 'VAR_1348', 'VAR_1349', 'VAR_1350', 'VAR_1351', 'VAR_1352', 'VAR_1353', 'VAR_1354', 'VAR_1355', 'VAR_1356', 'VAR_1357', 'VAR_1358', 'VAR_1359', 'VAR_1360', 'VAR_1361', 'VAR_1362', 'VAR_1363', 'VAR_1364', 'VAR_1365', 'VAR_1366', 'VAR_1367', 'VAR_1368', 'VAR_1369', 'VAR_1370', 'VAR_1371', 'VAR_1372', 'VAR_1373', 'VAR_1374', 'VAR_1375', 'VAR_1376', 'VAR_1377', 'VAR_1378', 'VAR_1379', 'VAR_1380', 'VAR_1381', 'VAR_1382', 'VAR_1383', 'VAR_1384', 'VAR_1385', 'VAR_1386', 'VAR_1387', 'VAR_1388', 'VAR_1389', 'VAR_1390', 'VAR_1391', 'VAR_1392', 'VAR_1393', 'VAR_1394', 'VAR_1395', 'VAR_1396', 'VAR_1397', 'VAR_1398', 'VAR_1399', 'VAR_1400', 'VAR_1401', 'VAR_1402', 'VAR_1403', 'VAR_1404', 'VAR_1405', 'VAR_1406', 'VAR_1407', 'VAR_1408', 'VAR_1409', 'VAR_1410', 'VAR_1411', 'VAR_1412', 'VAR_1413', 'VAR_1414', 'VAR_1415', 'VAR_1416', 'VAR_1417', 'VAR_1418', 'VAR_1419', 'VAR_1420', 'VAR_1421', 'VAR_1422', 'VAR_1423', 'VAR_1424', 'VAR_1425', 'VAR_1426', 'VAR_1427', 'VAR_1428', 'VAR_1429', 'VAR_1430', 'VAR_1431', 'VAR_1432', 'VAR_1433', 'VAR_1434', 'VAR_1435', 'VAR_1436', 'VAR_1437', 'VAR_1438', 'VAR_1439', 'VAR_1440', 'VAR_1441', 'VAR_1442', 'VAR_1443', 'VAR_1444', 'VAR_1445', 'VAR_1446', 'VAR_1447', 'VAR_1448', 'VAR_1449', 'VAR_1450', 'VAR_1451', 'VAR_1452', 'VAR_1453', 'VAR_1454', 'VAR_1455', 'VAR_1456', 'VAR_1457', 'VAR_1458', 'VAR_1459', 'VAR_1460', 'VAR_1461', 'VAR_1462', 'VAR_1463', 'VAR_1464', 'VAR_1465', 'VAR_1466', 'VAR_1467', 'VAR_1468', 'VAR_1469', 'VAR_1470', 'VAR_1471', 'VAR_1472', 'VAR_1473', 'VAR_1474', 'VAR_1475', 'VAR_1476', 'VAR_1477', 'VAR_1478', 'VAR_1479', 'VAR_1480', 'VAR_1481', 'VAR_1482', 'VAR_1483', 'VAR_1484', 'VAR_1485', 'VAR_1486', 'VAR_1487', 'VAR_1488', 'VAR_1489', 'VAR_1490', 'VAR_1491', 'VAR_1492', 'VAR_1493', 'VAR_1494', 'VAR_1495', 'VAR_1496', 'VAR_1497', 'VAR_1498', 'VAR_1499', 'VAR_1500', 'VAR_1501', 'VAR_1502', 'VAR_1503', 'VAR_1504', 'VAR_1505', 'VAR_1506', 'VAR_1507', 'VAR_1508', 'VAR_1509', 'VAR_1510', 'VAR_1511', 'VAR_1512', 'VAR_1513', 'VAR_1514', 'VAR_1515', 'VAR_1516', 'VAR_1517', 'VAR_1518', 'VAR_1519', 'VAR_1520', 'VAR_1521', 'VAR_1522', 'VAR_1523', 'VAR_1524', 'VAR_1525', 'VAR_1526', 'VAR_1527', 'VAR_1528', 'VAR_1529', 'VAR_1530', 'VAR_1531', 'VAR_1532', 'VAR_1533', 'VAR_1534', 'VAR_1535', 'VAR_1536', 'VAR_1537', 'VAR_1538', 'VAR_1539', 'VAR_1540', 'VAR_1541', 'VAR_1542', 'VAR_1543', 'VAR_1544', 'VAR_1545', 'VAR_1546', 'VAR_1547', 'VAR_1548', 'VAR_1549', 'VAR_1550', 'VAR_1551', 'VAR_1552', 'VAR_1553', 'VAR_1554', 'VAR_1555', 'VAR_1556', 'VAR_1557', 'VAR_1558', 'VAR_1559', 'VAR_1560', 'VAR_1561', 'VAR_1562', 'VAR_1563', 'VAR_1564', 'VAR_1565', 'VAR_1566', 'VAR_1567', 'VAR_1568', 'VAR_1569', 'VAR_1570', 'VAR_1571', 'VAR_1572', 'VAR_1573', 'VAR_1574', 'VAR_1575', 'VAR_1576', 'VAR_1577', 'VAR_1578', 'VAR_1579', 'VAR_1580', 'VAR_1581', 'VAR_1582', 'VAR_1583', 'VAR_1584', 'VAR_1585', 'VAR_1586', 'VAR_1587', 'VAR_1588', 'VAR_1589', 'VAR_1590', 'VAR_1591', 'VAR_1592', 'VAR_1593', 'VAR_1594', 'VAR_1595', 'VAR_1596', 'VAR_1597', 'VAR_1598', 'VAR_1599', 'VAR_1600', 'VAR_1601', 'VAR_1602', 'VAR_1603', 'VAR_1604', 'VAR_1605', 'VAR_1606', 'VAR_1607', 'VAR_1608', 'VAR_1609', 'VAR_1610', 'VAR_1611', 'VAR_1612', 'VAR_1613', 'VAR_1614', 'VAR_1615', 'VAR_1616', 'VAR_1617', 'VAR_1618', 'VAR_1619', 'VAR_1620', 'VAR_1621', 'VAR_1622', 'VAR_1623', 'VAR_1624', 'VAR_1625', 'VAR_1626', 'VAR_1627', 'VAR_1628', 'VAR_1629', 'VAR_1630', 'VAR_1631', 'VAR_1632', 'VAR_1633', 'VAR_1634', 'VAR_1635', 'VAR_1636', 'VAR_1637', 'VAR_1638', 'VAR_1639', 'VAR_1640', 'VAR_1641', 'VAR_1642', 'VAR_1643', 'VAR_1644', 'VAR_1645', 'VAR_1646', 'VAR_1647', 'VAR_1648', 'VAR_1649', 'VAR_1650', 'VAR_1651', 'VAR_1652', 'VAR_1653', 'VAR_1654', 'VAR_1655', 'VAR_1656', 'VAR_1657', 'VAR_1658', 'VAR_1659', 'VAR_1660', 'VAR_1661', 'VAR_1662', 'VAR_1663', 'VAR_1664', 'VAR_1665', 'VAR_1666', 'VAR_1667', 'VAR_1668', 'VAR_1669', 'VAR_1670', 'VAR_1671', 'VAR_1672', 'VAR_1673', 'VAR_1674', 'VAR_1675', 'VAR_1676', 'VAR_1677', 'VAR_1678', 'VAR_1679', 'VAR_1680', 'VAR_1681', 'VAR_1682', 'VAR_1683', 'VAR_1684', 'VAR_1685', 'VAR_1686', 'VAR_1687', 'VAR_1688', 'VAR_1689', 'VAR_1690', 'VAR_1691', 'VAR_1692', 'VAR_1693', 'VAR_1694', 'VAR_1695', 'VAR_1696', 'VAR_1697', 'VAR_1698', 'VAR_1699', 'VAR_1700', 'VAR_1701', 'VAR_1702', 'VAR_1703', 'VAR_1704', 'VAR_1705', 'VAR_1706', 'VAR_1707', 'VAR_1708', 'VAR_1709', 'VAR_1710', 'VAR_1711', 'VAR_1712', 'VAR_1713', 'VAR_1714', 'VAR_1715', 'VAR_1716', 'VAR_1717', 'VAR_1718', 'VAR_1719', 'VAR_1720', 'VAR_1721', 'VAR_1722', 'VAR_1723', 'VAR_1724', 'VAR_1725', 'VAR_1726', 'VAR_1727', 'VAR_1728', 'VAR_1729', 'VAR_1730', 'VAR_1731', 'VAR_1732', 'VAR_1733', 'VAR_1734', 'VAR_1735', 'VAR_1736', 'VAR_1737', 'VAR_1738', 'VAR_1739', 'VAR_1740', 'VAR_1741', 'VAR_1742', 'VAR_1743', 'VAR_1744', 'VAR_1745', 'VAR_1746', 'VAR_1747', 'VAR_1748', 'VAR_1749', 'VAR_1750', 'VAR_1751', 'VAR_1752', 'VAR_1753', 'VAR_1754', 'VAR_1755', 'VAR_1756', 'VAR_1757', 'VAR_1758', 'VAR_1759', 'VAR_1760', 'VAR_1761', 'VAR_1762', 'VAR_1763', 'VAR_1764', 'VAR_1765', 'VAR_1766', 'VAR_1767', 'VAR_1768', 'VAR_1769', 'VAR_1770', 'VAR_1771', 'VAR_1772', 'VAR_1773', 'VAR_1774', 'VAR_1775', 'VAR_1776', 'VAR_1777', 'VAR_1778', 'VAR_1779', 'VAR_1780', 'VAR_1781', 'VAR_1782', 'VAR_1783', 'VAR_1784', 'VAR_1785', 'VAR_1786', 'VAR_1787', 'VAR_1788', 'VAR_1789', 'VAR_1790', 'VAR_1791', 'VAR_1792', 'VAR_1793', 'VAR_1794', 'VAR_1795', 'VAR_1796', 'VAR_1797', 'VAR_1798', 'VAR_1799', 'VAR_1800', 'VAR_1801', 'VAR_1802', 'VAR_1803', 'VAR_1804', 'VAR_1805', 'VAR_1806', 'VAR_1807', 'VAR_1808', 'VAR_1809', 'VAR_1810', 'VAR_1811', 'VAR_1812', 'VAR_1813', 'VAR_1814', 'VAR_1815', 'VAR_1816', 'VAR_1817', 'VAR_1818', 'VAR_1819', 'VAR_1820', 'VAR_1821', 'VAR_1822', 'VAR_1823', 'VAR_1824', 'VAR_1825', 'VAR_1826', 'VAR_1827', 'VAR_1828', 'VAR_1829', 'VAR_1830', 'VAR_1831', 'VAR_1832', 'VAR_1833', 'VAR_1834', 'VAR_1835', 'VAR_1836', 'VAR_1837', 'VAR_1838', 'VAR_1839', 'VAR_1840', 'VAR_1841', 'VAR_1842', 'VAR_1843', 'VAR_1844', 'VAR_1845', 'VAR_1846', 'VAR_1847', 'VAR_1848', 'VAR_1849', 'VAR_1850', 'VAR_1851', 'VAR_1852', 'VAR_1853', 'VAR_1854', 'VAR_1855', 'VAR_1856', 'VAR_1857', 'VAR_1858', 'VAR_1859', 'VAR_1860', 'VAR_1861', 'VAR_1862', 'VAR_1863', 'VAR_1864', 'VAR_1865', 'VAR_1866', 'VAR_1867', 'VAR_1868', 'VAR_1869', 'VAR_1870', 'VAR_1871', 'VAR_1872', 'VAR_1873', 'VAR_1874', 'VAR_1875', 'VAR_1876', 'VAR_1877', 'VAR_1878', 'VAR_1879', 'VAR_1880', 'VAR_1881', 'VAR_1882', 'VAR_1883', 'VAR_1884', 'VAR_1885', 'VAR_1886', 'VAR_1887', 'VAR_1888', 'VAR_1889', 'VAR_1890', 'VAR_1891', 'VAR_1892', 'VAR_1893', 'VAR_1894', 'VAR_1895', 'VAR_1896', 'VAR_1897', 'VAR_1898', 'VAR_1899', 'VAR_1900', 'VAR_1901', 'VAR_1902', 'VAR_1903', 'VAR_1904', 'VAR_1905', 'VAR_1906', 'VAR_1907', 'VAR_1908', 'VAR_1909', 'VAR_1910', 'VAR_1911', 'VAR_1912', 'VAR_1913', 'VAR_1914', 'VAR_1915', 'VAR_1916', 'VAR_1917', 'VAR_1918', 'VAR_1919', 'VAR_1920', 'VAR_1921', 'VAR_1922', 'VAR_1923', 'VAR_1924', 'VAR_1925', 'VAR_1926', 'VAR_1927', 'VAR_1928', 'VAR_1929', 'VAR_1930', 'VAR_1931', 'VAR_1932', 'VAR_1933', 'target', 'is_train']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "# 异常值处理\n",
    "# 先统一替换\n",
    "X_train.replace('NaN', -999, inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's calculate how many times one feature is greater than the other and create cross tabel out of it. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "# select first 42 numeric features\n",
    "feats = num_cols[:42]\n",
    "\n",
    "# build 'mean(feat1 > feat2)' plot\n",
    "gt_matrix(feats,16)\n",
    "\n",
    "# 有一些列是很相似的\n",
    "\n",
    "# Indeed, we see interesting patterns here. \n",
    "# There are blocks of geatures where one is strictly greater than the other. \n",
    "# So we can hypothesize, that each column correspondes to cumulative counts, \n",
    "# e.g. feature number one is counts in first month, \n",
    "# second -- total count number in first two month and so on. \n",
    "# So we immediately understand what features we should generate to make tree-based models more efficient: \n",
    "# the differences between consecutive values."
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "## VAR_0002, VAR_0003 \n",
    "\n",
    "hist_it(X_train['VAR_0002'])\n",
    "plt.ylim((0,0.05))\n",
    "plt.xlim((-10,1010))\n",
    "\n",
    "hist_it(X_train['VAR_0003'])\n",
    "plt.ylim((0,0.03))\n",
    "plt.xlim((-10,1010))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(-10, 1010)"
      ]
     },
     "metadata": {},
     "execution_count": 32
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "scrolled": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "X_train['VAR_0002'].value_counts()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "12     167\n",
       "24     157\n",
       "36     129\n",
       "13     103\n",
       "60     103\n",
       "      ... \n",
       "828      1\n",
       "760      1\n",
       "704      1\n",
       "684      1\n",
       "235      1\n",
       "Name: VAR_0002, Length: 456, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "X_train['VAR_0003'].value_counts()\n",
    "# We see there is something special about 12, 24 and so on, sowe can create another feature x mod 12. "
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0      568\n",
       "24     118\n",
       "12     112\n",
       "60     112\n",
       "36      82\n",
       "      ... \n",
       "371      1\n",
       "385      1\n",
       "397      1\n",
       "603      1\n",
       "405      1\n",
       "Name: VAR_0003, Length: 385, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "## VAR_0004\n",
    "\n",
    "X_train['VAR_0004_mod50'] = X_train['VAR_0004'] % 50\n",
    "hist_it(X_train['VAR_0004_mod50'])\n",
    "plt.ylim((0,0.6))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0, 0.6)"
      ]
     },
     "metadata": {},
     "execution_count": 38
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "# 分类型特征\n",
    "X_train.loc[:,cat_cols].head().T"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>VAR_0001</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0005</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0044</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0073</td>\n",
       "      <td>-999</td>\n",
       "      <td>04SEP12:00:00:00</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0074</td>\n",
       "      <td>-999</td>\n",
       "      <td>5208</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0529</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0530</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0531</td>\n",
       "      <td>201111</td>\n",
       "      <td>201210</td>\n",
       "      <td>201112</td>\n",
       "      <td>201210</td>\n",
       "      <td>201110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_0840</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "      <td>-999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VAR_1934</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>IAPS</td>\n",
       "      <td>RCC</td>\n",
       "      <td>BRANCH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>327 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0                 1       2       3       4\n",
       "VAR_0001       H                 H       H       H       R\n",
       "VAR_0005       C                 B       C       C       N\n",
       "VAR_0044      []                []      []      []      []\n",
       "VAR_0073    -999  04SEP12:00:00:00    -999    -999    -999\n",
       "VAR_0074    -999              5208    -999    -999    -999\n",
       "...          ...               ...     ...     ...     ...\n",
       "VAR_0529       0                 0       0       0       0\n",
       "VAR_0530      -1                -1      -1      -1      -1\n",
       "VAR_0531  201111            201210  201112  201210  201110\n",
       "VAR_0840    -999              -999    -999    -999    -999\n",
       "VAR_1934    IAPS              IAPS    IAPS     RCC  BRANCH\n",
       "\n",
       "[327 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "for c in cat_cols:\n",
    "    print(c)\n",
    "    print(X_train[c].value_counts())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "VAR_0001\n",
      "R    2874\n",
      "H    2113\n",
      "Q      13\n",
      "Name: VAR_0001, dtype: int64\n",
      "VAR_0005\n",
      "B    2498\n",
      "C    1884\n",
      "N     540\n",
      "S      78\n",
      "Name: VAR_0005, dtype: int64\n",
      "VAR_0044\n",
      "[]    5000\n",
      "Name: VAR_0044, dtype: int64\n",
      "VAR_0073\n",
      "-999                3546\n",
      "11SEP12:00:00:00       9\n",
      "25NOV11:00:00:00       7\n",
      "06JUL12:00:00:00       7\n",
      "13MAR09:00:00:00       7\n",
      "                    ... \n",
      "16AUG10:00:00:00       1\n",
      "13JUN12:00:00:00       1\n",
      "20JUL10:00:00:00       1\n",
      "23MAR11:00:00:00       1\n",
      "27SEP10:00:00:00       1\n",
      "Name: VAR_0073, Length: 677, dtype: int64\n",
      "VAR_0074\n",
      "-999.0     3546\n",
      " 2500.0      98\n",
      " 2000.0      80\n",
      " 3000.0      76\n",
      " 1500.0      57\n",
      "           ... \n",
      " 2900.0       1\n",
      " 7842.0       1\n",
      " 1962.0       1\n",
      " 2060.0       1\n",
      " 7583.0       1\n",
      "Name: VAR_0074, Length: 518, dtype: int64\n",
      "VAR_0075\n",
      "22SEP10:00:00:00    53\n",
      "23SEP10:00:00:00    34\n",
      "07DEC11:00:00:00    34\n",
      "08NOV11:00:00:00    28\n",
      "23NOV11:00:00:00    27\n",
      "                    ..\n",
      "09FEB06:00:00:00     1\n",
      "15OCT10:00:00:00     1\n",
      "16FEB11:00:00:00     1\n",
      "29JUN09:00:00:00     1\n",
      "05FEB12:00:00:00     1\n",
      "Name: VAR_0075, Length: 1091, dtype: int64\n",
      "VAR_0156\n",
      "-999                4799\n",
      "13SEP11:00:00:00       5\n",
      "02JAN12:00:00:00       4\n",
      "03JUN11:00:00:00       3\n",
      "28DEC11:00:00:00       3\n",
      "                    ... \n",
      "17NOV11:00:00:00       1\n",
      "07SEP12:00:00:00       1\n",
      "24OCT11:00:00:00       1\n",
      "20JUL12:00:00:00       1\n",
      "09OCT12:00:00:00       1\n",
      "Name: VAR_0156, Length: 160, dtype: int64\n",
      "VAR_0157\n",
      "-999                4969\n",
      "31JUL12:00:00:00       1\n",
      "21AUG11:00:00:00       1\n",
      "05JUN11:00:00:00       1\n",
      "12FEB12:00:00:00       1\n",
      "13DEC11:00:00:00       1\n",
      "19JUN12:00:00:00       1\n",
      "25OCT11:00:00:00       1\n",
      "16JAN12:00:00:00       1\n",
      "27FEB12:00:00:00       1\n",
      "05JUL12:00:00:00       1\n",
      "19MAY12:00:00:00       1\n",
      "29APR12:00:00:00       1\n",
      "23MAY11:00:00:00       1\n",
      "09AUG12:00:00:00       1\n",
      "23NOV11:00:00:00       1\n",
      "26MAY12:00:00:00       1\n",
      "16APR12:00:00:00       1\n",
      "11AUG11:00:00:00       1\n",
      "09MAR12:00:00:00       1\n",
      "27JAN12:00:00:00       1\n",
      "15JUN11:00:00:00       1\n",
      "01JAN09:00:00:00       1\n",
      "27MAR12:00:00:00       1\n",
      "20JUL12:00:00:00       1\n",
      "13JUN12:00:00:00       1\n",
      "20APR12:00:00:00       1\n",
      "23JUL12:00:00:00       1\n",
      "07JUN12:00:00:00       1\n",
      "13OCT11:00:00:00       1\n",
      "31AUG11:00:00:00       1\n",
      "16JUN12:00:00:00       1\n",
      "Name: VAR_0157, dtype: int64\n",
      "VAR_0158\n",
      "-999                4928\n",
      "21MAR12:00:00:00       2\n",
      "15MAR12:00:00:00       2\n",
      "02MAR12:00:00:00       2\n",
      "24FEB12:00:00:00       2\n",
      "23NOV11:00:00:00       2\n",
      "09APR12:00:00:00       2\n",
      "01JUN12:00:00:00       2\n",
      "03FEB12:00:00:00       2\n",
      "01FEB12:00:00:00       2\n",
      "17APR12:00:00:00       2\n",
      "03JAN12:00:00:00       2\n",
      "13APR12:00:00:00       2\n",
      "29FEB12:00:00:00       2\n",
      "27FEB12:00:00:00       1\n",
      "01APR12:00:00:00       1\n",
      "07DEC11:00:00:00       1\n",
      "22JAN12:00:00:00       1\n",
      "07FEB12:00:00:00       1\n",
      "27APR12:00:00:00       1\n",
      "11JAN12:00:00:00       1\n",
      "29MAY12:00:00:00       1\n",
      "15MAY12:00:00:00       1\n",
      "28AUG12:00:00:00       1\n",
      "18JAN12:00:00:00       1\n",
      "16APR12:00:00:00       1\n",
      "18MAY12:00:00:00       1\n",
      "02DEC11:00:00:00       1\n",
      "30APR12:00:00:00       1\n",
      "31MAY12:00:00:00       1\n",
      "08FEB12:00:00:00       1\n",
      "24AUG12:00:00:00       1\n",
      "02APR12:00:00:00       1\n",
      "06JUN12:00:00:00       1\n",
      "28JAN12:00:00:00       1\n",
      "26NOV11:00:00:00       1\n",
      "05OCT11:00:00:00       1\n",
      "06JAN12:00:00:00       1\n",
      "16AUG12:00:00:00       1\n",
      "07MAY12:00:00:00       1\n",
      "23MAR12:00:00:00       1\n",
      "01MAR12:00:00:00       1\n",
      "16MAR12:00:00:00       1\n",
      "28JAN10:00:00:00       1\n",
      "26APR12:00:00:00       1\n",
      "25MAY12:00:00:00       1\n",
      "05MAR12:00:00:00       1\n",
      "22FEB12:00:00:00       1\n",
      "04MAY12:00:00:00       1\n",
      "02JAN12:00:00:00       1\n",
      "10MAR10:00:00:00       1\n",
      "11FEB12:00:00:00       1\n",
      "31JAN12:00:00:00       1\n",
      "08NOV11:00:00:00       1\n",
      "27JUN12:00:00:00       1\n",
      "31JUL12:00:00:00       1\n",
      "06APR12:00:00:00       1\n",
      "09MAR12:00:00:00       1\n",
      "04NOV11:00:00:00       1\n",
      "21MAY12:00:00:00       1\n",
      "Name: VAR_0158, dtype: int64\n",
      "VAR_0159\n",
      "-999                4799\n",
      "23MAY12:00:00:00       3\n",
      "04APR12:00:00:00       3\n",
      "04JUN12:00:00:00       3\n",
      "13APR12:00:00:00       3\n",
      "                    ... \n",
      "09JUN11:00:00:00       1\n",
      "20APR12:00:00:00       1\n",
      "13MAR12:00:00:00       1\n",
      "15AUG11:00:00:00       1\n",
      "09OCT12:00:00:00       1\n",
      "Name: VAR_0159, Length: 159, dtype: int64\n",
      "VAR_0166\n",
      "-999                4535\n",
      "20DEC11:00:00:00       4\n",
      "05OCT12:00:00:00       3\n",
      "21DEC11:00:00:00       3\n",
      "05JUL07:00:00:00       3\n",
      "                    ... \n",
      "16DEC08:00:00:00       1\n",
      "03FEB12:00:00:00       1\n",
      "21JUN12:00:00:00       1\n",
      "19JUN08:00:00:00       1\n",
      "16JUN11:00:00:00       1\n",
      "Name: VAR_0166, Length: 391, dtype: int64\n",
      "VAR_0167\n",
      "-999                4924\n",
      "27JUL12:00:00:00       2\n",
      "16FEB12:00:00:00       2\n",
      "03AUG12:00:00:00       2\n",
      "17JAN12:00:00:00       2\n",
      "                    ... \n",
      "15APR11:00:00:00       1\n",
      "01SEP11:00:00:00       1\n",
      "06MAR12:00:00:00       1\n",
      "27APR12:00:00:00       1\n",
      "15JUN11:00:00:00       1\n",
      "Name: VAR_0167, Length: 69, dtype: int64\n",
      "VAR_0168\n",
      "-999                4636\n",
      "03NOV11:00:00:00      10\n",
      "30SEP11:00:00:00       6\n",
      "20JAN12:00:00:00       6\n",
      "27APR12:00:00:00       4\n",
      "                    ... \n",
      "25DEC10:00:00:00       1\n",
      "04FEB08:00:00:00       1\n",
      "12JAN12:00:00:00       1\n",
      "17FEB11:00:00:00       1\n",
      "07MAR12:00:00:00       1\n",
      "Name: VAR_0168, Length: 279, dtype: int64\n",
      "VAR_0169\n",
      "-999                4535\n",
      "20DEC11:00:00:00       5\n",
      "21DEC11:00:00:00       4\n",
      "21MAY12:00:00:00       4\n",
      "05OCT12:00:00:00       4\n",
      "                    ... \n",
      "07APR12:00:00:00       1\n",
      "05OCT09:00:00:00       1\n",
      "06NOV09:00:00:00       1\n",
      "04OCT12:00:00:00       1\n",
      "08SEP11:00:00:00       1\n",
      "Name: VAR_0169, Length: 363, dtype: int64\n",
      "VAR_0176\n",
      "-999                4419\n",
      "08SEP11:00:00:00       5\n",
      "12DEC11:00:00:00       4\n",
      "14JUL11:00:00:00       4\n",
      "13SEP11:00:00:00       4\n",
      "                    ... \n",
      "25SEP11:00:00:00       1\n",
      "29SEP10:00:00:00       1\n",
      "15MAY07:00:00:00       1\n",
      "12AUG11:00:00:00       1\n",
      "16JUL08:00:00:00       1\n",
      "Name: VAR_0176, Length: 447, dtype: int64\n",
      "VAR_0177\n",
      "-999                4898\n",
      "27JUL12:00:00:00       2\n",
      "13DEC11:00:00:00       2\n",
      "14DEC11:00:00:00       2\n",
      "13APR12:00:00:00       2\n",
      "                    ... \n",
      "13JAN12:00:00:00       1\n",
      "21MAY11:00:00:00       1\n",
      "13JUN12:00:00:00       1\n",
      "20APR12:00:00:00       1\n",
      "07AUG10:00:00:00       1\n",
      "Name: VAR_0177, Length: 94, dtype: int64\n",
      "VAR_0178\n",
      "-999                4589\n",
      "03NOV11:00:00:00      10\n",
      "21FEB12:00:00:00       9\n",
      "30SEP11:00:00:00       6\n",
      "20JAN12:00:00:00       6\n",
      "                    ... \n",
      "03AUG09:00:00:00       1\n",
      "15JUN07:00:00:00       1\n",
      "25APR08:00:00:00       1\n",
      "03APR12:00:00:00       1\n",
      "19AUG12:00:00:00       1\n",
      "Name: VAR_0178, Length: 291, dtype: int64\n",
      "VAR_0179\n",
      "-999                4419\n",
      "13APR12:00:00:00       6\n",
      "20DEC11:00:00:00       5\n",
      "21NOV11:00:00:00       5\n",
      "17NOV11:00:00:00       4\n",
      "                    ... \n",
      "12JUL12:00:00:00       1\n",
      "19JUL11:00:00:00       1\n",
      "03MAY11:00:00:00       1\n",
      "06DEC10:00:00:00       1\n",
      "09OCT12:00:00:00       1\n",
      "Name: VAR_0179, Length: 410, dtype: int64\n",
      "VAR_0200\n",
      "CHICAGO           73\n",
      "HOUSTON           43\n",
      "COLUMBUS          31\n",
      "INDIANAPOLIS      30\n",
      "EL PASO           30\n",
      "                  ..\n",
      "FORT PAYNE         1\n",
      "COALINGA           1\n",
      "SHARPSBURG         1\n",
      "WEST FRANKFORT     1\n",
      "CHESNEE            1\n",
      "Name: VAR_0200, Length: 2255, dtype: int64\n",
      "VAR_0202\n",
      "BatchInquiry    5000\n",
      "Name: VAR_0202, dtype: int64\n",
      "VAR_0204\n",
      "31JAN14:18:30:00    13\n",
      "31JAN14:18:20:00    13\n",
      "29JAN14:19:21:00    12\n",
      "31JAN14:16:13:00    11\n",
      "31JAN14:23:50:00    11\n",
      "                    ..\n",
      "29JAN14:23:28:00     1\n",
      "30JAN14:18:37:00     1\n",
      "29JAN14:23:13:00     1\n",
      "31JAN14:15:51:00     1\n",
      "29JAN14:23:02:00     1\n",
      "Name: VAR_0204, Length: 1155, dtype: int64\n",
      "VAR_0205\n",
      "-999.0    4928\n",
      " 258.0       2\n",
      " 502.0       2\n",
      " 189.0       2\n",
      " 583.0       1\n",
      "          ... \n",
      " 253.0       1\n",
      " 684.0       1\n",
      " 772.0       1\n",
      " 489.0       1\n",
      " 58.0        1\n",
      "Name: VAR_0205, Length: 70, dtype: int64\n",
      "VAR_0206\n",
      "-999.0    4941\n",
      " 636.0       3\n",
      " 377.0       2\n",
      " 661.0       1\n",
      " 744.0       1\n",
      " 675.0       1\n",
      " 686.0       1\n",
      " 629.0       1\n",
      " 790.0       1\n",
      " 492.0       1\n",
      " 707.0       1\n",
      " 671.0       1\n",
      " 614.0       1\n",
      " 442.0       1\n",
      " 385.0       1\n",
      " 762.0       1\n",
      " 656.0       1\n",
      " 472.0       1\n",
      " 727.0       1\n",
      " 869.0       1\n",
      " 750.0       1\n",
      " 812.0       1\n",
      " 819.0       1\n",
      " 388.0       1\n",
      " 697.0       1\n",
      " 427.0       1\n",
      " 548.0       1\n",
      " 640.0       1\n",
      " 370.0       1\n",
      " 537.0       1\n",
      " 550.0       1\n",
      " 587.0       1\n",
      " 366.0       1\n",
      " 611.0       1\n",
      " 575.0       1\n",
      " 607.0       1\n",
      " 639.0       1\n",
      " 594.0       1\n",
      " 795.0       1\n",
      " 549.0       1\n",
      " 617.0       1\n",
      " 752.0       1\n",
      " 383.0       1\n",
      " 511.0       1\n",
      " 393.0       1\n",
      " 415.0       1\n",
      " 723.0       1\n",
      " 766.0       1\n",
      " 801.0       1\n",
      " 868.0       1\n",
      " 616.0       1\n",
      " 597.0       1\n",
      " 623.0       1\n",
      " 690.0       1\n",
      " 379.0       1\n",
      " 779.0       1\n",
      " 552.0       1\n",
      "Name: VAR_0206, dtype: int64\n",
      "VAR_0207\n",
      "-999    5000\n",
      "Name: VAR_0207, dtype: int64\n",
      "VAR_0208\n",
      "-999.0    4365\n",
      " 3.0        18\n",
      " 1.0        18\n",
      " 2.0        12\n",
      " 5.0        11\n",
      "          ... \n",
      " 253.0       1\n",
      " 153.0       1\n",
      " 330.0       1\n",
      " 191.0       1\n",
      " 283.0       1\n",
      "Name: VAR_0208, Length: 245, dtype: int64\n",
      "VAR_0209\n",
      "-999.0    4691\n",
      " 1.0         5\n",
      " 3.0         4\n",
      " 409.0       3\n",
      " 614.0       3\n",
      "          ... \n",
      " 630.0       1\n",
      " 860.0       1\n",
      " 335.0       1\n",
      " 378.0       1\n",
      " 492.0       1\n",
      "Name: VAR_0209, Length: 250, dtype: int64\n",
      "VAR_0210\n",
      "-999.0    4365\n",
      " 3.0        18\n",
      " 1.0        18\n",
      " 2.0        12\n",
      " 5.0        11\n",
      "          ... \n",
      " 253.0       1\n",
      " 153.0       1\n",
      " 330.0       1\n",
      " 191.0       1\n",
      " 283.0       1\n",
      "Name: VAR_0210, Length: 245, dtype: int64\n",
      "VAR_0211\n",
      "-999.0    4365\n",
      " 3.0        18\n",
      " 1.0        18\n",
      " 2.0        12\n",
      " 5.0        11\n",
      "          ... \n",
      " 253.0       1\n",
      " 153.0       1\n",
      " 330.0       1\n",
      " 191.0       1\n",
      " 283.0       1\n",
      "Name: VAR_0211, Length: 245, dtype: int64\n",
      "VAR_0212\n",
      "-9.990000e+02    439\n",
      " 6.413219e+10      2\n",
      " 7.841525e+10      2\n",
      " 4.101791e+10      2\n",
      " 2.383169e+10      2\n",
      "                ... \n",
      " 9.411820e+10      1\n",
      " 3.793215e+10      1\n",
      " 2.880598e+10      1\n",
      " 1.512010e+10      1\n",
      " 9.030319e+10      1\n",
      "Name: VAR_0212, Length: 4557, dtype: int64\n",
      "VAR_0213\n",
      "-999    5000\n",
      "Name: VAR_0213, dtype: int64\n",
      "VAR_0214\n",
      "-999    5000\n",
      "Name: VAR_0214, dtype: int64\n",
      "VAR_0216\n",
      "DS    5000\n",
      "Name: VAR_0216, dtype: int64\n",
      "VAR_0217\n",
      "07DEC11:02:00:00    37\n",
      "06DEC11:02:00:00    34\n",
      "05JUN12:02:00:00    33\n",
      "23OCT12:02:00:00    33\n",
      "05SEP12:02:00:00    32\n",
      "                    ..\n",
      "29JAN12:02:00:00     1\n",
      "26FEB12:02:00:00     1\n",
      "25NOV11:02:00:00     1\n",
      "02JAN12:02:00:00     1\n",
      "15JAN12:02:00:00     1\n",
      "Name: VAR_0217, Length: 390, dtype: int64\n",
      "VAR_0222\n",
      "C6    5000\n",
      "Name: VAR_0222, dtype: int64\n",
      "VAR_0237\n",
      "CA    532\n",
      "TX    501\n",
      "NC    399\n",
      "IL    294\n",
      "GA    273\n",
      "VA    260\n",
      "PA    242\n",
      "OH    236\n",
      "FL    233\n",
      "IN    225\n",
      "SC    196\n",
      "AL    182\n",
      "TN    181\n",
      "WA    154\n",
      "MO    112\n",
      "KY    106\n",
      "LA    102\n",
      "CO     89\n",
      "OK     85\n",
      "MS     84\n",
      "WI     77\n",
      "OR     64\n",
      "WV     61\n",
      "NM     53\n",
      "NY     51\n",
      "ID     37\n",
      "MI     34\n",
      "AZ     31\n",
      "MD     24\n",
      "NJ     21\n",
      "KS     16\n",
      "HI     10\n",
      "WY      8\n",
      "IA      7\n",
      "UT      6\n",
      "MT      5\n",
      "NV      4\n",
      "NE      2\n",
      "SD      2\n",
      "DE      1\n",
      "Name: VAR_0237, dtype: int64\n",
      "VAR_0242\n",
      "359.0    58\n",
      "360.0    47\n",
      "350.0    44\n",
      "351.0    41\n",
      "358.0    41\n",
      "         ..\n",
      "421.0     1\n",
      "431.0     1\n",
      "498.0     1\n",
      "514.0     1\n",
      "399.0     1\n",
      "Name: VAR_0242, Length: 465, dtype: int64\n",
      "VAR_0243\n",
      " 1.0      4924\n",
      "-999.0      38\n",
      "-1.0        12\n",
      " 5.0         4\n",
      " 7.0         4\n",
      " 47.0        2\n",
      " 3.0         2\n",
      " 2.0         2\n",
      " 24.0        1\n",
      " 10.0        1\n",
      " 22.0        1\n",
      " 79.0        1\n",
      " 6.0         1\n",
      " 20.0        1\n",
      " 41.0        1\n",
      " 17.0        1\n",
      " 61.0        1\n",
      " 25.0        1\n",
      " 93.0        1\n",
      " 4.0         1\n",
      "Name: VAR_0243, dtype: int64\n",
      "VAR_0244\n",
      " 1.0      4939\n",
      "-999.0      38\n",
      " 0.0        23\n",
      "Name: VAR_0244, dtype: int64\n",
      "VAR_0245\n",
      " 1.0      1644\n",
      " 2.0      1118\n",
      " 3.0       917\n",
      " 4.0       676\n",
      " 5.0       335\n",
      " 6.0       117\n",
      " 0.0        94\n",
      " 7.0        42\n",
      "-999.0      38\n",
      " 8.0        16\n",
      " 9.0         2\n",
      " 10.0        1\n",
      "Name: VAR_0245, dtype: int64\n",
      "VAR_0246\n",
      "-1.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0246, dtype: int64\n",
      "VAR_0247\n",
      " 0.0      4949\n",
      "-999.0      38\n",
      " 1.0        13\n",
      "Name: VAR_0247, dtype: int64\n",
      "VAR_0248\n",
      " 0.0      4952\n",
      "-999.0      38\n",
      " 1.0        10\n",
      "Name: VAR_0248, dtype: int64\n",
      "VAR_0249\n",
      " 3.0      4920\n",
      "-999.0      38\n",
      " 1.0        19\n",
      " 0.0        12\n",
      " 2.0        11\n",
      "Name: VAR_0249, dtype: int64\n",
      "VAR_0250\n",
      " 2.0      4922\n",
      "-999.0      38\n",
      " 0.0        29\n",
      "-1.0        10\n",
      " 1.0         1\n",
      "Name: VAR_0250, dtype: int64\n",
      "VAR_0251\n",
      " 0.0      4049\n",
      " 2.0       843\n",
      "-1.0        42\n",
      "-999.0      38\n",
      " 1.0        28\n",
      "Name: VAR_0251, dtype: int64\n",
      "VAR_0252\n",
      " 1.0      4498\n",
      " 0.0       464\n",
      "-999.0      38\n",
      "Name: VAR_0252, dtype: int64\n",
      "VAR_0253\n",
      " 7.0      4102\n",
      " 0.0       522\n",
      " 4.0       185\n",
      " 5.0        60\n",
      " 3.0        57\n",
      "-999.0      38\n",
      " 6.0        31\n",
      " 2.0         5\n",
      "Name: VAR_0253, dtype: int64\n",
      "VAR_0254\n",
      "41.0     146\n",
      "44.0     135\n",
      "40.0     132\n",
      "35.0     128\n",
      "31.0     126\n",
      "        ... \n",
      "97.0       1\n",
      "91.0       1\n",
      "86.0       1\n",
      "84.0       1\n",
      "107.0      1\n",
      "Name: VAR_0254, Length: 78, dtype: int64\n",
      "VAR_0255\n",
      "-1.0      479\n",
      " 41.0     146\n",
      " 44.0     134\n",
      " 40.0     132\n",
      " 35.0     129\n",
      "         ... \n",
      " 87.0       1\n",
      " 84.0       1\n",
      " 97.0       1\n",
      " 86.0       1\n",
      " 107.0      1\n",
      "Name: VAR_0255, Length: 76, dtype: int64\n",
      "VAR_0256\n",
      " 1.0      3586\n",
      " 2.0       999\n",
      " 3.0       244\n",
      " 4.0        72\n",
      "-999.0      38\n",
      " 5.0        21\n",
      " 0.0        18\n",
      " 6.0        11\n",
      " 8.0         3\n",
      " 10.0        2\n",
      " 12.0        1\n",
      " 7.0         1\n",
      " 9.0         1\n",
      " 16.0        1\n",
      " 15.0        1\n",
      " 11.0        1\n",
      "Name: VAR_0256, dtype: int64\n",
      "VAR_0257\n",
      " 6.0      444\n",
      " 8.0      391\n",
      " 7.0      379\n",
      " 5.0      362\n",
      " 3.0      353\n",
      " 4.0      346\n",
      " 9.0      331\n",
      " 10.0     307\n",
      " 2.0      261\n",
      " 12.0     253\n",
      " 11.0     249\n",
      " 13.0     202\n",
      " 14.0     180\n",
      " 15.0     142\n",
      " 1.0      140\n",
      " 16.0     120\n",
      " 17.0      88\n",
      " 19.0      74\n",
      " 18.0      73\n",
      " 20.0      51\n",
      "-999.0     38\n",
      " 21.0      36\n",
      " 22.0      29\n",
      " 23.0      25\n",
      " 24.0      20\n",
      " 25.0      20\n",
      " 27.0      18\n",
      " 26.0      12\n",
      " 28.0      12\n",
      " 0.0       10\n",
      " 30.0       8\n",
      " 31.0       4\n",
      " 29.0       4\n",
      " 33.0       3\n",
      " 32.0       3\n",
      " 34.0       3\n",
      " 40.0       2\n",
      " 46.0       2\n",
      " 38.0       1\n",
      " 39.0       1\n",
      " 35.0       1\n",
      " 36.0       1\n",
      " 37.0       1\n",
      "Name: VAR_0257, dtype: int64\n",
      "VAR_0258\n",
      " 0.0      3422\n",
      " 1.0      1359\n",
      " 2.0       159\n",
      "-999.0      38\n",
      " 3.0        20\n",
      " 4.0         2\n",
      "Name: VAR_0258, dtype: int64\n",
      "VAR_0259\n",
      " 0.0      4927\n",
      "-999.0      38\n",
      " 1.0        34\n",
      " 2.0         1\n",
      "Name: VAR_0259, dtype: int64\n",
      "VAR_0260\n",
      " 0.0      3416\n",
      " 1.0      1137\n",
      " 2.0       286\n",
      " 3.0       104\n",
      "-999.0      38\n",
      " 4.0        16\n",
      " 5.0         3\n",
      "Name: VAR_0260, dtype: int64\n",
      "VAR_0261\n",
      " 0.0      4659\n",
      " 1.0       288\n",
      "-999.0      38\n",
      " 2.0        14\n",
      " 3.0         1\n",
      "Name: VAR_0261, dtype: int64\n",
      "VAR_0262\n",
      " 1.0      2790\n",
      " 2.0      1458\n",
      " 3.0       496\n",
      " 4.0       152\n",
      "-999.0      38\n",
      " 5.0        36\n",
      " 6.0        12\n",
      " 0.0         8\n",
      " 7.0         5\n",
      " 8.0         3\n",
      " 13.0        1\n",
      " 9.0         1\n",
      "Name: VAR_0262, dtype: int64\n",
      "VAR_0263\n",
      " 5.0      464\n",
      " 7.0      461\n",
      " 6.0      445\n",
      " 4.0      406\n",
      " 9.0      378\n",
      " 8.0      369\n",
      " 3.0      344\n",
      " 10.0     311\n",
      " 11.0     256\n",
      " 2.0      253\n",
      " 12.0     211\n",
      " 13.0     207\n",
      " 1.0      144\n",
      " 14.0     138\n",
      " 15.0     115\n",
      " 16.0     100\n",
      " 17.0      69\n",
      " 18.0      68\n",
      " 19.0      43\n",
      "-999.0     38\n",
      " 20.0      36\n",
      " 21.0      33\n",
      " 22.0      30\n",
      " 23.0      22\n",
      " 24.0      17\n",
      " 0.0       13\n",
      " 25.0      11\n",
      " 27.0       4\n",
      " 29.0       3\n",
      " 26.0       3\n",
      " 33.0       2\n",
      " 28.0       2\n",
      " 40.0       1\n",
      " 30.0       1\n",
      " 32.0       1\n",
      " 39.0       1\n",
      "Name: VAR_0263, dtype: int64\n",
      "VAR_0264\n",
      " 0.0      4958\n",
      "-999.0      38\n",
      " 1.0         4\n",
      "Name: VAR_0264, dtype: int64\n",
      "VAR_0265\n",
      " 0.0      4780\n",
      " 1.0       178\n",
      "-999.0      38\n",
      " 2.0         3\n",
      " 3.0         1\n",
      "Name: VAR_0265, dtype: int64\n",
      "VAR_0266\n",
      " 0.0      2087\n",
      " 1.0      1851\n",
      " 2.0       343\n",
      " 3.0       105\n",
      " 4.0        81\n",
      " 6.0        60\n",
      " 5.0        54\n",
      " 9.0        48\n",
      " 7.0        44\n",
      "-999.0      38\n",
      " 8.0        36\n",
      " 11.0       29\n",
      " 10.0       28\n",
      " 14.0       26\n",
      " 12.0       25\n",
      " 13.0       21\n",
      " 15.0       19\n",
      " 17.0       16\n",
      " 16.0       14\n",
      " 21.0       11\n",
      " 18.0       10\n",
      " 22.0       10\n",
      " 20.0        8\n",
      " 19.0        7\n",
      " 24.0        4\n",
      " 23.0        4\n",
      " 26.0        4\n",
      " 30.0        3\n",
      " 34.0        3\n",
      " 27.0        2\n",
      " 25.0        2\n",
      " 28.0        2\n",
      " 40.0        1\n",
      " 53.0        1\n",
      " 33.0        1\n",
      " 42.0        1\n",
      " 35.0        1\n",
      "Name: VAR_0266, dtype: int64\n",
      "VAR_0267\n",
      " 0.0      4006\n",
      " 1.0       626\n",
      " 2.0       140\n",
      " 3.0        54\n",
      " 4.0        40\n",
      "-999.0      38\n",
      " 5.0        28\n",
      " 6.0        21\n",
      " 7.0         9\n",
      " 8.0         9\n",
      " 9.0         5\n",
      " 11.0        4\n",
      " 14.0        4\n",
      " 12.0        3\n",
      " 37.0        2\n",
      " 34.0        1\n",
      " 31.0        1\n",
      " 27.0        1\n",
      " 10.0        1\n",
      " 15.0        1\n",
      " 20.0        1\n",
      " 25.0        1\n",
      " 13.0        1\n",
      " 16.0        1\n",
      " 22.0        1\n",
      " 18.0        1\n",
      "Name: VAR_0267, dtype: int64\n",
      "VAR_0268\n",
      " 0.0      4102\n",
      " 1.0       736\n",
      " 2.0       115\n",
      "-999.0      38\n",
      " 3.0         9\n",
      "Name: VAR_0268, dtype: int64\n",
      "VAR_0269\n",
      " 0.0      4752\n",
      " 1.0       189\n",
      "-999.0      38\n",
      " 2.0        21\n",
      "Name: VAR_0269, dtype: int64\n",
      "VAR_0270\n",
      "-1.0      4957\n",
      "-999.0      38\n",
      " 39.0        1\n",
      " 9.0         1\n",
      " 16.0        1\n",
      " 13.0        1\n",
      " 0.0         1\n",
      "Name: VAR_0270, dtype: int64\n",
      "VAR_0271\n",
      " 0.0      4957\n",
      "-999.0      38\n",
      " 1.0         5\n",
      "Name: VAR_0271, dtype: int64\n",
      "VAR_0272\n",
      "301.0    69\n",
      "313.0    53\n",
      "350.0    45\n",
      "310.0    42\n",
      "435.0    39\n",
      "         ..\n",
      "77.0      1\n",
      "929.0     1\n",
      "683.0     1\n",
      "193.0     1\n",
      "136.0     1\n",
      "Name: VAR_0272, Length: 640, dtype: int64\n",
      "VAR_0273\n",
      "289.0    63\n",
      "301.0    46\n",
      "423.0    44\n",
      "298.0    41\n",
      "277.0    40\n",
      "         ..\n",
      "95.0      1\n",
      "68.0      1\n",
      "116.0     1\n",
      "706.0     1\n",
      "633.0     1\n",
      "Name: VAR_0273, Length: 614, dtype: int64\n",
      "VAR_0274\n",
      "CA      541\n",
      "TX      410\n",
      "IL      314\n",
      "NC      290\n",
      "OH      257\n",
      "PA      237\n",
      "NY      210\n",
      "IN      205\n",
      "GA      197\n",
      "FL      196\n",
      "VA      194\n",
      "SC      178\n",
      "AL      176\n",
      "TN      139\n",
      "LA      129\n",
      "KY      102\n",
      "MS       98\n",
      "MO       96\n",
      "MI       93\n",
      "WA       92\n",
      "WI       75\n",
      "OK       71\n",
      "WV       69\n",
      "NJ       65\n",
      "OR       64\n",
      "MD       57\n",
      "CO       53\n",
      "NM       45\n",
      "-999     38\n",
      "AZ       30\n",
      "KS       29\n",
      "PR       28\n",
      "ID       27\n",
      "AR       22\n",
      "IA       20\n",
      "HI       17\n",
      "MN       15\n",
      "DC       13\n",
      "MA       12\n",
      "CT       12\n",
      "NE        9\n",
      "WY        9\n",
      "UT        8\n",
      "ME        8\n",
      "NV        7\n",
      "DE        7\n",
      "SD        6\n",
      "-1        6\n",
      "EE        5\n",
      "ND        4\n",
      "VT        4\n",
      "GS        3\n",
      "MT        3\n",
      "RN        2\n",
      "AK        1\n",
      "RI        1\n",
      "NH        1\n",
      "Name: VAR_0274, dtype: int64\n",
      "VAR_0275\n",
      " 0.0      4957\n",
      "-999.0      38\n",
      " 1.0         5\n",
      "Name: VAR_0275, dtype: int64\n",
      "VAR_0276\n",
      " 0.0      4934\n",
      "-999.0      38\n",
      " 1.0        28\n",
      "Name: VAR_0276, dtype: int64\n",
      "VAR_0277\n",
      " 0.0      4958\n",
      "-999.0      38\n",
      " 1.0         4\n",
      "Name: VAR_0277, dtype: int64\n",
      "VAR_0278\n",
      " 0.0      4944\n",
      "-999.0      38\n",
      " 4.0         7\n",
      " 5.0         5\n",
      " 2.0         3\n",
      " 1.0         3\n",
      "Name: VAR_0278, dtype: int64\n",
      "VAR_0279\n",
      "-1.0      495\n",
      " 5.0       78\n",
      " 1.0       74\n",
      " 3.0       73\n",
      " 8.0       73\n",
      "         ... \n",
      " 284.0      1\n",
      " 309.0      1\n",
      " 960.0      1\n",
      " 304.0      1\n",
      " 348.0      1\n",
      "Name: VAR_0279, Length: 392, dtype: int64\n",
      "VAR_0280\n",
      " 1.0      4315\n",
      "-1.0       495\n",
      "-999.0      38\n",
      " 3.0        10\n",
      " 8.0         8\n",
      "          ... \n",
      " 18.0        1\n",
      " 132.0       1\n",
      " 30.0        1\n",
      " 57.0        1\n",
      " 59.0        1\n",
      "Name: VAR_0280, Length: 62, dtype: int64\n",
      "VAR_0281\n",
      " 2.0      3499\n",
      " 1.0       970\n",
      "-1.0       493\n",
      "-999.0      38\n",
      "Name: VAR_0281, dtype: int64\n",
      "VAR_0282\n",
      "-1.0      495\n",
      " 0.0      101\n",
      " 4.0       80\n",
      " 7.0       73\n",
      " 2.0       73\n",
      "         ... \n",
      " 332.0      1\n",
      " 361.0      1\n",
      " 476.0      1\n",
      " 243.0      1\n",
      " 317.0      1\n",
      "Name: VAR_0282, Length: 391, dtype: int64\n",
      "VAR_0283\n",
      "S       3922\n",
      "H        827\n",
      "-1       162\n",
      "-999      38\n",
      "P         21\n",
      "R         14\n",
      "F         13\n",
      "U          3\n",
      "Name: VAR_0283, dtype: int64\n",
      "VAR_0284\n",
      "-1.0      4911\n",
      "-999.0      38\n",
      " 4.0        37\n",
      " 1.0        11\n",
      " 3.0         3\n",
      "Name: VAR_0284, dtype: int64\n",
      "VAR_0285\n",
      " 0.0      1855\n",
      "-1.0      1568\n",
      " 1.0      1539\n",
      "-999.0      38\n",
      "Name: VAR_0285, dtype: int64\n",
      "VAR_0286\n",
      " 0.0      2912\n",
      "-1.0      1568\n",
      " 1.0       482\n",
      "-999.0      38\n",
      "Name: VAR_0286, dtype: int64\n",
      "VAR_0287\n",
      " 0.0      2463\n",
      "-1.0      1568\n",
      " 1.0       931\n",
      "-999.0      38\n",
      "Name: VAR_0287, dtype: int64\n",
      "VAR_0288\n",
      "-1.0      1922\n",
      " 64.0       42\n",
      " 11.0       38\n",
      "-999.0      38\n",
      " 16.0       36\n",
      "          ... \n",
      " 599.0       1\n",
      " 322.0       1\n",
      " 613.0       1\n",
      " 281.0       1\n",
      " 317.0       1\n",
      "Name: VAR_0288, Length: 347, dtype: int64\n",
      "VAR_0289\n",
      "-1.0         4033\n",
      "-999.0         38\n",
      " 75000.0       10\n",
      " 40000.0        9\n",
      " 65000.0        9\n",
      "             ... \n",
      " 165500.0       1\n",
      " 290000.0       1\n",
      " 111100.0       1\n",
      " 282000.0       1\n",
      " 208892.0       1\n",
      "Name: VAR_0289, Length: 557, dtype: int64\n",
      "VAR_0290\n",
      "-1.0      4219\n",
      " 3.0       590\n",
      " 1.0        96\n",
      " 2.0        57\n",
      "-999.0      38\n",
      "Name: VAR_0290, dtype: int64\n",
      "VAR_0291\n",
      " 0.0      3499\n",
      " 1.0      1369\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0291, dtype: int64\n",
      "VAR_0292\n",
      " 0.0      3582\n",
      " 1.0      1380\n",
      "-999.0      38\n",
      "Name: VAR_0292, dtype: int64\n",
      "VAR_0293\n",
      " 0.0         2083\n",
      "-999.0         38\n",
      " 204000.0       4\n",
      " 97000.0        4\n",
      " 43000.0        4\n",
      "             ... \n",
      " 220393.0       1\n",
      " 65480.0        1\n",
      " 79520.0        1\n",
      " 70260.0        1\n",
      " 74120.0        1\n",
      "Name: VAR_0293, Length: 2637, dtype: int64\n",
      "VAR_0294\n",
      " 2012.0    1745\n",
      "-1.0       1700\n",
      " 2011.0    1155\n",
      " 2010.0     149\n",
      " 2008.0      51\n",
      " 2009.0      44\n",
      "-999.0       38\n",
      " 2007.0      32\n",
      " 2006.0      22\n",
      " 2005.0      19\n",
      " 2002.0      12\n",
      " 2003.0      11\n",
      " 2004.0       8\n",
      " 2000.0       6\n",
      " 2001.0       6\n",
      " 1996.0       1\n",
      " 1999.0       1\n",
      "Name: VAR_0294, dtype: int64\n",
      "VAR_0295\n",
      " 0.0         2787\n",
      "-999.0         38\n",
      " 85000.0        8\n",
      " 97000.0        5\n",
      " 82500.0        5\n",
      "             ... \n",
      " 50686.0        1\n",
      " 193848.0       1\n",
      " 35800.0        1\n",
      " 114763.0       1\n",
      " 261119.0       1\n",
      "Name: VAR_0295, Length: 1941, dtype: int64\n",
      "VAR_0296\n",
      " 0.0         2704\n",
      "-999.0         38\n",
      " 75000.0       12\n",
      " 120000.0      10\n",
      " 55000.0       10\n",
      "             ... \n",
      " 64408.0        1\n",
      " 169400.0       1\n",
      " 193842.0       1\n",
      " 60200.0        1\n",
      " 68100.0        1\n",
      "Name: VAR_0296, Length: 1905, dtype: int64\n",
      "VAR_0297\n",
      " 0.0         2818\n",
      "-999.0         38\n",
      " 90000.0       16\n",
      " 75000.0       12\n",
      " 40000.0       10\n",
      "             ... \n",
      " 68304.0        1\n",
      " 75714.0        1\n",
      " 148822.0       1\n",
      " 134448.0       1\n",
      " 199661.0       1\n",
      "Name: VAR_0297, Length: 1813, dtype: int64\n",
      "VAR_0298\n",
      " 0.0         3135\n",
      "-999.0         38\n",
      " 115000.0       7\n",
      " 80000.0        6\n",
      " 550000.0       6\n",
      "             ... \n",
      " 154900.0       1\n",
      " 72331.0        1\n",
      " 165596.0       1\n",
      " 79500.0        1\n",
      " 148500.0       1\n",
      "Name: VAR_0298, Length: 1588, dtype: int64\n",
      "VAR_0299\n",
      " 0.00      2704\n",
      "-999.00      38\n",
      " 0.98        31\n",
      " 0.80        28\n",
      " 0.94        27\n",
      "           ... \n",
      " 2.26         1\n",
      " 0.09         1\n",
      " 2.73         1\n",
      " 2.18         1\n",
      " 1.77         1\n",
      "Name: VAR_0299, Length: 263, dtype: int64\n",
      "VAR_0300\n",
      " 0.00      2728\n",
      " 1.00       124\n",
      "-999.00      38\n",
      " 0.94        37\n",
      " 0.89        36\n",
      "           ... \n",
      " 2.06         1\n",
      " 2.68         1\n",
      " 1.89         1\n",
      " 2.60         1\n",
      " 3.90         1\n",
      "Name: VAR_0300, Length: 241, dtype: int64\n",
      "VAR_0301\n",
      "0.00     2708\n",
      "1.00       79\n",
      "0.98       42\n",
      "1.06       41\n",
      "0.90       39\n",
      "         ... \n",
      "2.20        1\n",
      "3.16        1\n",
      "3.57        1\n",
      "2.02        1\n",
      "13.30       1\n",
      "Name: VAR_0301, Length: 257, dtype: int64\n",
      "VAR_0302\n",
      "1.0      125\n",
      "3.0      106\n",
      "5.0      104\n",
      "8.0       98\n",
      "2.0       97\n",
      "        ... \n",
      "0.0        1\n",
      "387.0      1\n",
      "371.0      1\n",
      "331.0      1\n",
      "348.0      1\n",
      "Name: VAR_0302, Length: 383, dtype: int64\n",
      "VAR_0303\n",
      " 1.0      4803\n",
      "-1.0        95\n",
      "-999.0      38\n",
      " 2.0         8\n",
      " 7.0         6\n",
      " 5.0         5\n",
      " 6.0         3\n",
      " 17.0        2\n",
      " 3.0         2\n",
      " 24.0        2\n",
      " 93.0        2\n",
      " 47.0        2\n",
      " 41.0        2\n",
      " 78.0        2\n",
      " 38.0        1\n",
      " 22.0        1\n",
      " 181.0       1\n",
      " 61.0        1\n",
      " 9.0         1\n",
      " 79.0        1\n",
      " 8.0         1\n",
      " 15.0        1\n",
      " 71.0        1\n",
      " 109.0       1\n",
      " 10.0        1\n",
      " 32.0        1\n",
      " 145.0       1\n",
      " 128.0       1\n",
      " 25.0        1\n",
      " 224.0       1\n",
      " 4.0         1\n",
      " 0.0         1\n",
      " 11.0        1\n",
      " 20.0        1\n",
      " 174.0       1\n",
      " 23.0        1\n",
      " 39.0        1\n",
      " 133.0       1\n",
      " 27.0        1\n",
      " 42.0        1\n",
      " 21.0        1\n",
      " 77.0        1\n",
      "Name: VAR_0303, dtype: int64\n",
      "VAR_0304\n",
      "0.0      138\n",
      "2.0      107\n",
      "4.0      105\n",
      "1.0       99\n",
      "7.0       95\n",
      "        ... \n",
      "576.0      1\n",
      "370.0      1\n",
      "464.0      1\n",
      "441.0      1\n",
      "283.0      1\n",
      "Name: VAR_0304, Length: 382, dtype: int64\n",
      "VAR_0305\n",
      "S       3702\n",
      "H        877\n",
      "P        260\n",
      "-1       104\n",
      "-999      38\n",
      "R         15\n",
      "U          3\n",
      "M          1\n",
      "Name: VAR_0305, dtype: int64\n",
      "VAR_0306\n",
      " 0.0      3519\n",
      " 1.0      1349\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0306, dtype: int64\n",
      "VAR_0307\n",
      " 0.0      4412\n",
      " 1.0       456\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0307, dtype: int64\n",
      "VAR_0308\n",
      " 0.0      3934\n",
      " 1.0       934\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0308, dtype: int64\n",
      "VAR_0309\n",
      "-1.0      2132\n",
      " 11.0       39\n",
      "-999.0      38\n",
      " 64.0       37\n",
      " 16.0       35\n",
      "          ... \n",
      " 178.0       1\n",
      " 276.0       1\n",
      " 211.0       1\n",
      " 181.0       1\n",
      " 340.0       1\n",
      "Name: VAR_0309, Length: 355, dtype: int64\n",
      "VAR_0310\n",
      "-1.0         2132\n",
      " 0.0         1948\n",
      "-999.0         38\n",
      " 40000.0        9\n",
      " 65000.0        9\n",
      "             ... \n",
      " 98950.0        1\n",
      " 399900.0       1\n",
      " 75400.0        1\n",
      " 195859.0       1\n",
      " 127500.0       1\n",
      "Name: VAR_0310, Length: 552, dtype: int64\n",
      "VAR_0311\n",
      "-1.0      4295\n",
      " 3.0       535\n",
      " 1.0        80\n",
      " 2.0        52\n",
      "-999.0      38\n",
      "Name: VAR_0311, dtype: int64\n",
      "VAR_0312\n",
      " 0.0      3546\n",
      " 1.0      1322\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0312, dtype: int64\n",
      "VAR_0313\n",
      " 0.0         2174\n",
      "-1.0           94\n",
      "-999.0         38\n",
      " 204000.0       5\n",
      " 43000.0        5\n",
      "             ... \n",
      " 25988.0        1\n",
      " 7674.0         1\n",
      " 144680.0       1\n",
      " 291624.0       1\n",
      " 290000.0       1\n",
      "Name: VAR_0313, Length: 2477, dtype: int64\n",
      "VAR_0314\n",
      "-1.0       1926\n",
      " 2012.0    1605\n",
      " 2011.0    1069\n",
      " 2010.0     150\n",
      " 2009.0      47\n",
      " 2008.0      41\n",
      "-999.0       38\n",
      " 2007.0      37\n",
      " 2006.0      21\n",
      " 2005.0      20\n",
      " 2003.0      12\n",
      " 2000.0       9\n",
      " 2002.0       9\n",
      " 2004.0       7\n",
      " 2001.0       6\n",
      " 1999.0       2\n",
      " 1996.0       1\n",
      "Name: VAR_0314, dtype: int64\n",
      "VAR_0315\n",
      " 0.0         2852\n",
      "-1.0           94\n",
      "-999.0         38\n",
      " 82500.0        6\n",
      " 85000.0        6\n",
      "             ... \n",
      " 211060.0       1\n",
      " 30613.0        1\n",
      " 144680.0       1\n",
      " 49104.0        1\n",
      " 134134.0       1\n",
      "Name: VAR_0315, Length: 1836, dtype: int64\n",
      "VAR_0316\n",
      " 0.0         2782\n",
      "-1.0           94\n",
      "-999.0         38\n",
      " 50000.0       11\n",
      " 75000.0        9\n",
      "             ... \n",
      " 152795.0       1\n",
      " 51434.0        1\n",
      " 92593.0        1\n",
      " 163039.0       1\n",
      " 70782.0        1\n",
      "Name: VAR_0316, Length: 1765, dtype: int64\n",
      "VAR_0317\n",
      " 0.0         2881\n",
      "-1.0           94\n",
      "-999.0         38\n",
      " 90000.0       11\n",
      " 75000.0       10\n",
      "             ... \n",
      " 123563.0       1\n",
      " 134448.0       1\n",
      " 96473.0        1\n",
      " 142500.0       1\n",
      " 359578.0       1\n",
      "Name: VAR_0317, Length: 1696, dtype: int64\n",
      "VAR_0318\n",
      " 0.0         3184\n",
      "-1.0           94\n",
      "-999.0         38\n",
      " 220000.0       7\n",
      " 150000.0       7\n",
      "             ... \n",
      " 97053.0        1\n",
      " 67241.0        1\n",
      " 197971.0       1\n",
      " 187733.0       1\n",
      " 17000.0        1\n",
      "Name: VAR_0318, Length: 1463, dtype: int64\n",
      "VAR_0319\n",
      " 0.00      2782\n",
      "-1.00        94\n",
      "-999.00      38\n",
      " 0.98        27\n",
      " 0.78        27\n",
      "           ... \n",
      " 1.97         1\n",
      " 0.12         1\n",
      " 3.20         1\n",
      " 6.89         1\n",
      " 0.07         1\n",
      "Name: VAR_0319, Length: 254, dtype: int64\n",
      "VAR_0320\n",
      " 0.00      2801\n",
      " 1.00       123\n",
      "-1.00        94\n",
      " 0.98        38\n",
      "-999.00      38\n",
      "           ... \n",
      " 2.28         1\n",
      " 8.68         1\n",
      " 2.80         1\n",
      " 1.77         1\n",
      " 2.57         1\n",
      "Name: VAR_0320, Length: 243, dtype: int64\n",
      "VAR_0321\n",
      " 0.00      2786\n",
      "-1.00        94\n",
      " 1.00        84\n",
      " 0.98        38\n",
      "-999.00      38\n",
      "           ... \n",
      " 3.23         1\n",
      " 3.94         1\n",
      " 2.10         1\n",
      " 29.50        1\n",
      " 2.26         1\n",
      "Name: VAR_0321, Length: 249, dtype: int64\n",
      "VAR_0322\n",
      "-1.0      237\n",
      " 11.0      48\n",
      " 12.0      46\n",
      " 70.0      45\n",
      " 69.0      44\n",
      "         ... \n",
      " 545.0      1\n",
      " 385.0      1\n",
      " 403.0      1\n",
      " 488.0      1\n",
      " 349.0      1\n",
      "Name: VAR_0322, Length: 386, dtype: int64\n",
      "VAR_0323\n",
      " 1.0      2011\n",
      "-1.0       237\n",
      " 4.0        84\n",
      " 6.0        79\n",
      " 2.0        68\n",
      "          ... \n",
      " 261.0       1\n",
      " 251.0       1\n",
      " 151.0       1\n",
      " 142.0       1\n",
      " 177.0       1\n",
      "Name: VAR_0323, Length: 243, dtype: int64\n",
      "VAR_0324\n",
      " 0.0      394\n",
      "-1.0      237\n",
      " 4.0       81\n",
      " 6.0       68\n",
      " 68.0      62\n",
      "         ... \n",
      " 272.0      1\n",
      " 841.0      1\n",
      " 336.0      1\n",
      " 544.0      1\n",
      " 475.0      1\n",
      "Name: VAR_0324, Length: 364, dtype: int64\n",
      "VAR_0325\n",
      "S       3099\n",
      "H        906\n",
      "-1       471\n",
      "P        363\n",
      "R         82\n",
      "-999      38\n",
      "F         30\n",
      "M          6\n",
      "G          3\n",
      "U          2\n",
      "Name: VAR_0325, dtype: int64\n",
      "VAR_0326\n",
      " 0.0      4181\n",
      " 1.0       567\n",
      "-1.0       214\n",
      "-999.0      38\n",
      "Name: VAR_0326, dtype: int64\n",
      "VAR_0327\n",
      " 0.0      4315\n",
      " 1.0       433\n",
      "-1.0       214\n",
      "-999.0      38\n",
      "Name: VAR_0327, dtype: int64\n",
      "VAR_0328\n",
      " 0.0      3724\n",
      " 1.0      1024\n",
      "-1.0       214\n",
      "-999.0      38\n",
      "Name: VAR_0328, dtype: int64\n",
      "VAR_0329\n",
      "-1.0      2769\n",
      "-999.0      38\n",
      " 1.0        33\n",
      " 2.0        33\n",
      " 35.0       29\n",
      "          ... \n",
      " 293.0       1\n",
      " 242.0       1\n",
      " 268.0       1\n",
      " 250.0       1\n",
      " 305.0       1\n",
      "Name: VAR_0329, Length: 355, dtype: int64\n",
      "VAR_0330\n",
      "-1.0         2769\n",
      " 0.0         1554\n",
      "-999.0         38\n",
      " 120000.0       7\n",
      " 75000.0        6\n",
      "             ... \n",
      " 43000.0        1\n",
      " 101999.0       1\n",
      " 51000.0        1\n",
      " 417000.0       1\n",
      " 85500.0        1\n",
      "Name: VAR_0330, Length: 431, dtype: int64\n",
      "VAR_0331\n",
      " 0.0         2661\n",
      "-1.0          214\n",
      "-999.0         38\n",
      " 90000.0        4\n",
      " 100000.0       4\n",
      "             ... \n",
      " 284000.0       1\n",
      " 72300.0        1\n",
      " 136500.0       1\n",
      " 81800.0        1\n",
      " 46439.0        1\n",
      "Name: VAR_0331, Length: 1959, dtype: int64\n",
      "VAR_0332\n",
      "-1.0       2605\n",
      " 2012.0    1243\n",
      " 2011.0     802\n",
      " 2010.0     116\n",
      " 2008.0      47\n",
      " 2009.0      40\n",
      "-999.0       38\n",
      " 2007.0      28\n",
      " 2005.0      21\n",
      " 2006.0      16\n",
      " 2004.0      12\n",
      " 2002.0      11\n",
      " 2003.0       9\n",
      " 2001.0       7\n",
      " 2000.0       2\n",
      " 1999.0       2\n",
      " 1996.0       1\n",
      "Name: VAR_0332, dtype: int64\n",
      "VAR_0333\n",
      " 0.0         3192\n",
      "-1.0          214\n",
      "-999.0         38\n",
      " 52500.0        4\n",
      " 51600.0        4\n",
      "             ... \n",
      " 14268.0        1\n",
      " 108414.0       1\n",
      " 144700.0       1\n",
      " 85663.0        1\n",
      " 46671.0        1\n",
      "Name: VAR_0333, Length: 1442, dtype: int64\n",
      "VAR_0334\n",
      " 0.0         3133\n",
      "-1.0          214\n",
      "-999.0         38\n",
      " 65000.0       10\n",
      " 110000.0       7\n",
      "             ... \n",
      " 204110.0       1\n",
      " 27562.0        1\n",
      " 144721.0       1\n",
      " 62326.0        1\n",
      " 8500.0         1\n",
      "Name: VAR_0334, Length: 1390, dtype: int64\n",
      "VAR_0335\n",
      " 0.00      3133\n",
      "-1.00       214\n",
      "-999.00      38\n",
      " 0.93        24\n",
      " 0.72        22\n",
      "           ... \n",
      " 3.93         1\n",
      " 2.99         1\n",
      " 1.83         1\n",
      " 2.33         1\n",
      " 3.21         1\n",
      "Name: VAR_0335, Length: 249, dtype: int64\n",
      "VAR_0336\n",
      " 0.00      3146\n",
      "-1.00       214\n",
      " 1.00       106\n",
      "-999.00      38\n",
      " 0.87        27\n",
      "           ... \n",
      " 0.15         1\n",
      " 4.11         1\n",
      " 3.03         1\n",
      " 1.98         1\n",
      " 6.44         1\n",
      "Name: VAR_0336, Length: 232, dtype: int64\n",
      "VAR_0337\n",
      " 0.00      3134\n",
      "-1.00       214\n",
      " 1.00        61\n",
      "-999.00      38\n",
      " 0.90        31\n",
      "           ... \n",
      " 1.76         1\n",
      " 2.06         1\n",
      " 4.28         1\n",
      " 2.34         1\n",
      " 2.49         1\n",
      "Name: VAR_0337, Length: 233, dtype: int64\n",
      "VAR_0338\n",
      " 0.0       1284\n",
      "-1.0       1058\n",
      " 4.0        198\n",
      " 3.0        192\n",
      " 2.0        168\n",
      "           ... \n",
      " 1164.0       1\n",
      " 5524.0       1\n",
      " 943.0        1\n",
      " 1851.0       1\n",
      " 6110.0       1\n",
      "Name: VAR_0338, Length: 557, dtype: int64\n",
      "VAR_0339\n",
      " 0.0      3386\n",
      "-1.0      1052\n",
      " 1.0       524\n",
      "-999.0      38\n",
      "Name: VAR_0339, dtype: int64\n",
      "VAR_0340\n",
      "-1.0        3853\n",
      "-999.0        38\n",
      " 0.0           5\n",
      "-4000.0        3\n",
      " 16300.0       2\n",
      "            ... \n",
      "-23464.0       1\n",
      " 1433.0        1\n",
      " 4462.0        1\n",
      " 1595.0        1\n",
      " 68300.0       1\n",
      "Name: VAR_0340, Length: 1081, dtype: int64\n",
      "VAR_0341\n",
      "-1.0      1052\n",
      "-999.0      38\n",
      " 11.0       38\n",
      " 12.0       38\n",
      " 70.0       37\n",
      "          ... \n",
      " 287.0       1\n",
      " 357.0       1\n",
      " 381.0       1\n",
      " 638.0       1\n",
      " 413.0       1\n",
      "Name: VAR_0341, Length: 378, dtype: int64\n",
      "VAR_0342\n",
      "-1      1052\n",
      "FF       719\n",
      "EE       413\n",
      "FE       390\n",
      "EF       308\n",
      "DD       306\n",
      "ED       258\n",
      "FD       239\n",
      "DE       218\n",
      "DF       200\n",
      "UU       157\n",
      "CE        72\n",
      "CD        71\n",
      "CC        64\n",
      "DC        63\n",
      "UF        61\n",
      "EC        48\n",
      "-999      38\n",
      "CF        35\n",
      "UD        32\n",
      "UE        30\n",
      "FC        30\n",
      "FU        25\n",
      "EU        18\n",
      "BC        14\n",
      "DU        13\n",
      "BD        13\n",
      "BE        11\n",
      "EB        11\n",
      "CB        11\n",
      "BB         9\n",
      "AD         8\n",
      "DB         8\n",
      "DA         7\n",
      "EA         6\n",
      "BF         6\n",
      "UC         5\n",
      "AA         5\n",
      "AE         4\n",
      "AC         4\n",
      "FA         4\n",
      "AF         4\n",
      "CA         4\n",
      "AB         3\n",
      "UA         1\n",
      "BA         1\n",
      "FB         1\n",
      "Name: VAR_0342, dtype: int64\n",
      "VAR_0343\n",
      " 2.0      1630\n",
      "-1.0      1209\n",
      " 3.0      1037\n",
      " 1.0       744\n",
      " 4.0       271\n",
      " 5.0        51\n",
      "-999.0      38\n",
      " 6.0        20\n",
      "Name: VAR_0343, dtype: int64\n",
      "VAR_0344\n",
      " 0.0      4517\n",
      " 1.0       445\n",
      "-999.0      38\n",
      "Name: VAR_0344, dtype: int64\n",
      "VAR_0345\n",
      "-1.0      4526\n",
      " 0.0       324\n",
      " 1.0       112\n",
      "-999.0      38\n",
      "Name: VAR_0345, dtype: int64\n",
      "VAR_0346\n",
      "-1.0      4526\n",
      " 1.0       322\n",
      " 0.0       114\n",
      "-999.0      38\n",
      "Name: VAR_0346, dtype: int64\n",
      "VAR_0347\n",
      "-1.0      4526\n",
      " 0.0       434\n",
      "-999.0      38\n",
      " 1.0         2\n",
      "Name: VAR_0347, dtype: int64\n",
      "VAR_0348\n",
      "-1.0      4526\n",
      " 0.0       392\n",
      " 1.0        44\n",
      "-999.0      38\n",
      "Name: VAR_0348, dtype: int64\n",
      "VAR_0349\n",
      "-1.0      4517\n",
      " 0.0       324\n",
      " 2.0        56\n",
      "-999.0      38\n",
      " 3.0        22\n",
      " 7.0        16\n",
      " 5.0        13\n",
      " 4.0        10\n",
      " 6.0         3\n",
      " 1.0         1\n",
      "Name: VAR_0349, dtype: int64\n",
      "VAR_0350\n",
      "-1.0      4517\n",
      " 4.0       144\n",
      " 5.0       111\n",
      " 3.0       105\n",
      " 6.0        63\n",
      "-999.0      39\n",
      " 2.0        19\n",
      " 1.0         2\n",
      "Name: VAR_0350, dtype: int64\n",
      "VAR_0351\n",
      " 6.0      1752\n",
      " 5.0      1097\n",
      " 4.0       841\n",
      " 3.0       570\n",
      " 2.0       370\n",
      " 1.0       320\n",
      "-999.0      38\n",
      " 0.0        12\n",
      "Name: VAR_0351, dtype: int64\n",
      "VAR_0352\n",
      "U       1982\n",
      "O       1818\n",
      "R       1068\n",
      "-1        94\n",
      "-999      38\n",
      "Name: VAR_0352, dtype: int64\n",
      "VAR_0353\n",
      "U       2415\n",
      "R       1317\n",
      "O       1016\n",
      "-1       214\n",
      "-999      38\n",
      "Name: VAR_0353, dtype: int64\n",
      "VAR_0354\n",
      "U       2345\n",
      "R       1206\n",
      "-1       847\n",
      "O        564\n",
      "-999      38\n",
      "Name: VAR_0354, dtype: int64\n",
      "VAR_0355\n",
      " 0.0      4625\n",
      " 1.0       307\n",
      "-999.0      38\n",
      " 2.0        21\n",
      " 3.0         9\n",
      "Name: VAR_0355, dtype: int64\n",
      "VAR_0356\n",
      " 0.0      4175\n",
      " 1.0       654\n",
      " 2.0        98\n",
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      " 3.0        34\n",
      " 4.0         1\n",
      "Name: VAR_0356, dtype: int64\n",
      "VAR_0357\n",
      " 0.0      3416\n",
      " 1.0      1137\n",
      " 2.0       286\n",
      " 3.0       104\n",
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      " 4.0        16\n",
      " 5.0         3\n",
      "Name: VAR_0357, dtype: int64\n",
      "VAR_0358\n",
      " 0.0      2573\n",
      " 1.0      1519\n",
      " 2.0       563\n",
      " 3.0       235\n",
      " 4.0        57\n",
      "-999.0      38\n",
      " 5.0        11\n",
      " 6.0         2\n",
      " 7.0         2\n",
      "Name: VAR_0358, dtype: int64\n",
      "VAR_0359\n",
      " 1.0      1738\n",
      " 0.0      1481\n",
      " 2.0       933\n",
      " 3.0       511\n",
      " 4.0       198\n",
      " 5.0        67\n",
      "-999.0      38\n",
      " 6.0        24\n",
      " 7.0         7\n",
      " 8.0         3\n",
      "Name: VAR_0359, dtype: int64\n",
      "VAR_0360\n",
      " 1.0      1262\n",
      " 2.0      1179\n",
      " 3.0       833\n",
      " 4.0       536\n",
      " 0.0       441\n",
      " 5.0       302\n",
      " 6.0       189\n",
      " 7.0       106\n",
      " 8.0        56\n",
      "-999.0      38\n",
      " 9.0        29\n",
      " 10.0       15\n",
      " 12.0        6\n",
      " 11.0        4\n",
      " 14.0        2\n",
      " 22.0        1\n",
      " 13.0        1\n",
      "Name: VAR_0360, dtype: int64\n",
      "VAR_0361\n",
      "28000.0     326\n",
      "26000.0     311\n",
      "29000.0     298\n",
      "27000.0     290\n",
      "30000.0     289\n",
      "           ... \n",
      "131000.0      1\n",
      "187000.0      1\n",
      "155000.0      1\n",
      "128000.0      1\n",
      "110000.0      1\n",
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      "VAR_0362\n",
      " 0.0      3029\n",
      " 1.0      1933\n",
      "-999.0      38\n",
      "Name: VAR_0362, dtype: int64\n",
      "VAR_0363\n",
      " 0.0      3029\n",
      " 1.0      1474\n",
      " 2.0       344\n",
      " 3.0        71\n",
      "-999.0      38\n",
      " 4.0        20\n",
      " 5.0         8\n",
      " 6.0         7\n",
      " 8.0         6\n",
      " 16.0        1\n",
      " 7.0         1\n",
      " 10.0        1\n",
      "Name: VAR_0363, dtype: int64\n",
      "VAR_0364\n",
      " 0.0         3601\n",
      "-999.0         38\n",
      " 85000.0        3\n",
      " 86300.0        3\n",
      " 141500.0       3\n",
      "             ... \n",
      " 58300.0        1\n",
      " 123560.0       1\n",
      " 21200.0        1\n",
      " 101370.0       1\n",
      " 160810.0       1\n",
      "Name: VAR_0364, Length: 1281, dtype: int64\n",
      "VAR_0365\n",
      " 0.0      2686\n",
      " 1.0      1487\n",
      " 2.0       534\n",
      " 3.0       160\n",
      " 4.0        41\n",
      "-999.0      38\n",
      " 5.0        17\n",
      " 6.0        16\n",
      " 7.0         8\n",
      " 8.0         4\n",
      " 15.0        2\n",
      " 9.0         2\n",
      " 11.0        2\n",
      " 12.0        2\n",
      " 16.0        1\n",
      "Name: VAR_0365, dtype: int64\n",
      "VAR_0366\n",
      "-1.0      2956\n",
      "-999.0      38\n",
      " 37.0       19\n",
      " 126.0      18\n",
      " 53.0       17\n",
      "          ... \n",
      " 362.0       1\n",
      " 253.0       1\n",
      " 374.0       1\n",
      " 275.0       1\n",
      " 317.0       1\n",
      "Name: VAR_0366, Length: 363, dtype: int64\n",
      "VAR_0367\n",
      "-1.0      2956\n",
      "-999.0      38\n",
      " 78.0       20\n",
      " 9.0        20\n",
      " 37.0       20\n",
      "          ... \n",
      " 249.0       1\n",
      " 302.0       1\n",
      " 375.0       1\n",
      " 269.0       1\n",
      " 327.0       1\n",
      "Name: VAR_0367, Length: 324, dtype: int64\n",
      "VAR_0368\n",
      "-1.0      4254\n",
      "-999.0      38\n",
      " 59.0       12\n",
      " 2.0        11\n",
      " 83.0       11\n",
      "          ... \n",
      " 122.0       1\n",
      " 148.0       1\n",
      " 175.0       1\n",
      " 185.0       1\n",
      " 197.0       1\n",
      "Name: VAR_0368, Length: 193, dtype: int64\n",
      "VAR_0369\n",
      "-1.0         4641\n",
      "-999.0         38\n",
      " 135000.0       5\n",
      " 165000.0       5\n",
      " 100000.0       5\n",
      "             ... \n",
      " 113258.0       1\n",
      " 183500.0       1\n",
      " 120421.0       1\n",
      " 3795.0         1\n",
      " 171000.0       1\n",
      "Name: VAR_0369, Length: 265, dtype: int64\n",
      "VAR_0370\n",
      "-1.00      4641\n",
      " 0.00       163\n",
      "-999.00      38\n",
      " 1.00         9\n",
      " 1.12         4\n",
      "           ... \n",
      " 2.81         1\n",
      " 2.05         1\n",
      " 0.17         1\n",
      " 2.64         1\n",
      " 1.88         1\n",
      "Name: VAR_0370, Length: 107, dtype: int64\n",
      "VAR_0371\n",
      " 0.0      4959\n",
      "-999.0      38\n",
      " 1.0         3\n",
      "Name: VAR_0371, dtype: int64\n",
      "VAR_0372\n",
      " 0.0      4942\n",
      "-999.0      38\n",
      " 1.0        19\n",
      " 2.0         1\n",
      "Name: VAR_0372, dtype: int64\n",
      "VAR_0373\n",
      " 0.0      4906\n",
      " 1.0        53\n",
      "-999.0      38\n",
      " 2.0         2\n",
      " 3.0         1\n",
      "Name: VAR_0373, dtype: int64\n",
      "VAR_0374\n",
      " 0.0      4822\n",
      " 1.0       133\n",
      "-999.0      38\n",
      " 2.0         6\n",
      " 3.0         1\n",
      "Name: VAR_0374, dtype: int64\n",
      "VAR_0375\n",
      " 0.0      4658\n",
      " 1.0       293\n",
      "-999.0      38\n",
      " 2.0         8\n",
      " 3.0         3\n",
      "Name: VAR_0375, dtype: int64\n",
      "VAR_0376\n",
      " 0.0      4235\n",
      " 1.0       657\n",
      " 2.0        55\n",
      "-999.0      38\n",
      " 3.0        12\n",
      " 4.0         2\n",
      " 6.0         1\n",
      "Name: VAR_0376, dtype: int64\n",
      "VAR_0377\n",
      " 0.0      4960\n",
      "-999.0      38\n",
      " 1.0         2\n",
      "Name: VAR_0377, dtype: int64\n",
      "VAR_0378\n",
      " 0.0      4947\n",
      "-999.0      38\n",
      " 1.0        15\n",
      "Name: VAR_0378, dtype: int64\n",
      "VAR_0379\n",
      " 0.0      4931\n",
      "-999.0      38\n",
      " 1.0        31\n",
      "Name: VAR_0379, dtype: int64\n",
      "VAR_0380\n",
      " 0.0      4904\n",
      " 1.0        58\n",
      "-999.0      38\n",
      "Name: VAR_0380, dtype: int64\n",
      "VAR_0381\n",
      " 0.0      4849\n",
      " 1.0       112\n",
      "-999.0      38\n",
      " 3.0         1\n",
      "Name: VAR_0381, dtype: int64\n",
      "VAR_0382\n",
      " 0.0      4650\n",
      " 1.0       288\n",
      "-999.0      38\n",
      " 2.0        18\n",
      " 3.0         5\n",
      " 5.0         1\n",
      "Name: VAR_0382, dtype: int64\n",
      "VAR_0383\n",
      " 0.0      2979\n",
      " 1.0      1983\n",
      "-999.0      38\n",
      "Name: VAR_0383, dtype: int64\n",
      "VAR_0384\n",
      " 0.0      4835\n",
      " 1.0       127\n",
      "-999.0      38\n",
      "Name: VAR_0384, dtype: int64\n",
      "VAR_0385\n",
      " 0.0      4835\n",
      " 1.0        73\n",
      "-999.0      38\n",
      " 2.0        28\n",
      " 3.0        12\n",
      " 4.0         5\n",
      " 5.0         4\n",
      " 6.0         3\n",
      " 34.0        1\n",
      " 10.0        1\n",
      "Name: VAR_0385, dtype: int64\n",
      "VAR_0386\n",
      " 0.0      4960\n",
      "-999.0      38\n",
      " 1.0         2\n",
      "Name: VAR_0386, dtype: int64\n",
      "VAR_0387\n",
      " 0.0      4955\n",
      "-999.0      38\n",
      " 1.0         4\n",
      " 2.0         2\n",
      " 4.0         1\n",
      "Name: VAR_0387, dtype: int64\n",
      "VAR_0388\n",
      " 0.0      4950\n",
      "-999.0      38\n",
      " 1.0         7\n",
      " 2.0         4\n",
      " 4.0         1\n",
      "Name: VAR_0388, dtype: int64\n",
      "VAR_0389\n",
      " 0.0      4940\n",
      "-999.0      38\n",
      " 1.0        13\n",
      " 2.0         8\n",
      " 6.0         1\n",
      "Name: VAR_0389, dtype: int64\n",
      "VAR_0390\n",
      " 0.0      4924\n",
      "-999.0      38\n",
      " 1.0        26\n",
      " 2.0         7\n",
      " 3.0         4\n",
      " 8.0         1\n",
      "Name: VAR_0390, dtype: int64\n",
      "VAR_0391\n",
      " 0.0      4897\n",
      " 1.0        39\n",
      "-999.0      38\n",
      " 2.0        16\n",
      " 3.0         7\n",
      " 5.0         2\n",
      " 16.0        1\n",
      "Name: VAR_0391, dtype: int64\n",
      "VAR_0392\n",
      " 0.0      4960\n",
      "-999.0      38\n",
      " 1.0         2\n",
      "Name: VAR_0392, dtype: int64\n",
      "VAR_0393\n",
      " 0.0      4960\n",
      "-999.0      38\n",
      " 2.0         1\n",
      " 1.0         1\n",
      "Name: VAR_0393, dtype: int64\n",
      "VAR_0394\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0394, dtype: int64\n",
      "VAR_0395\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0395, dtype: int64\n",
      "VAR_0396\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0396, dtype: int64\n",
      "VAR_0397\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0397, dtype: int64\n",
      "VAR_0398\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0398, dtype: int64\n",
      "VAR_0399\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0399, dtype: int64\n",
      "VAR_0400\n",
      "-1.0      2154\n",
      " 3.0      1028\n",
      " 4.0       711\n",
      " 2.0       518\n",
      " 5.0       269\n",
      " 1.0       262\n",
      "-999.0      38\n",
      " 6.0        20\n",
      "Name: VAR_0400, dtype: int64\n",
      "VAR_0401\n",
      " 0.0      4487\n",
      " 1.0       473\n",
      "-999.0      38\n",
      "-1.0         2\n",
      "Name: VAR_0401, dtype: int64\n",
      "VAR_0402\n",
      " 0.0      4814\n",
      " 1.0       146\n",
      "-999.0      38\n",
      "-1.0         2\n",
      "Name: VAR_0402, dtype: int64\n",
      "VAR_0403\n",
      "-1.0      4522\n",
      "-999.0      38\n",
      " 79.0       10\n",
      " 64.0        9\n",
      " 56.0        7\n",
      "          ... \n",
      " 869.0       1\n",
      " 234.0       1\n",
      " 443.0       1\n",
      " 573.0       1\n",
      " 654.0       1\n",
      "Name: VAR_0403, Length: 151, dtype: int64\n",
      "VAR_0404\n",
      "-1                      4476\n",
      "CONTACT                   73\n",
      "PRESIDENT                 48\n",
      "AGENT                     41\n",
      "-999                      38\n",
      "                        ... \n",
      "CLERK AND DATA ENTRY       1\n",
      "UP-COMING EVENTS           1\n",
      "OFFICER                    1\n",
      "FILER                      1\n",
      "HEAD                       1\n",
      "Name: VAR_0404, Length: 142, dtype: int64\n",
      "VAR_0405\n",
      " 0.0      3307\n",
      " 3.0       788\n",
      " 2.0       561\n",
      " 4.0       234\n",
      "-999.0      38\n",
      " 1.0        37\n",
      " 5.0        35\n",
      "Name: VAR_0405, dtype: int64\n",
      "VAR_0406\n",
      " 0.0      3307\n",
      " 1.0       835\n",
      " 2.0       309\n",
      " 3.0       147\n",
      " 4.0       117\n",
      " 5.0        62\n",
      " 6.0        52\n",
      "-999.0      38\n",
      " 7.0        34\n",
      " 8.0        25\n",
      " 11.0       13\n",
      " 10.0       11\n",
      " 14.0        9\n",
      " 9.0         9\n",
      " 18.0        6\n",
      " 12.0        5\n",
      " 16.0        4\n",
      " 17.0        4\n",
      " 20.0        2\n",
      " 13.0        2\n",
      " 19.0        2\n",
      " 24.0        1\n",
      " 22.0        1\n",
      " 15.0        1\n",
      " 61.0        1\n",
      " 21.0        1\n",
      " 38.0        1\n",
      " 87.0        1\n",
      "Name: VAR_0406, dtype: int64\n",
      "VAR_0407\n",
      " 0.0      4656\n",
      " 1.0       181\n",
      " 2.0        77\n",
      "-999.0      38\n",
      " 4.0        23\n",
      " 3.0        10\n",
      " 5.0         4\n",
      " 6.0         4\n",
      " 8.0         1\n",
      " 16.0        1\n",
      " 10.0        1\n",
      " 9.0         1\n",
      " 12.0        1\n",
      " 11.0        1\n",
      " 7.0         1\n",
      "Name: VAR_0407, dtype: int64\n",
      "VAR_0408\n",
      "-1.0      3307\n",
      "-999.0      38\n",
      " 4.0        35\n",
      " 1.0        34\n",
      " 26.0       31\n",
      "          ... \n",
      " 95.0        3\n",
      " 98.0        2\n",
      " 92.0        2\n",
      " 99.0        2\n",
      " 97.0        1\n",
      "Name: VAR_0408, Length: 102, dtype: int64\n",
      "VAR_0409\n",
      " 0.0      4927\n",
      "-999.0      38\n",
      " 1.0        27\n",
      " 3.0         3\n",
      " 4.0         2\n",
      " 2.0         2\n",
      " 10.0        1\n",
      "Name: VAR_0409, dtype: int64\n",
      "VAR_0410\n",
      "-1.0      4927\n",
      "-999.0      38\n",
      " 50.0        2\n",
      " 82.0        2\n",
      " 38.0        2\n",
      " 39.0        2\n",
      " 8.0         2\n",
      " 36.0        1\n",
      " 12.0        1\n",
      " 28.0        1\n",
      " 44.0        1\n",
      " 60.0        1\n",
      " 9.0         1\n",
      " 23.0        1\n",
      " 4.0         1\n",
      " 84.0        1\n",
      " 42.0        1\n",
      " 56.0        1\n",
      " 79.0        1\n",
      " 65.0        1\n",
      " 37.0        1\n",
      " 83.0        1\n",
      " 34.0        1\n",
      " 74.0        1\n",
      " 54.0        1\n",
      " 62.0        1\n",
      " 77.0        1\n",
      " 55.0        1\n",
      " 61.0        1\n",
      " 63.0        1\n",
      " 57.0        1\n",
      " 70.0        1\n",
      "Name: VAR_0410, dtype: int64\n",
      "VAR_0411\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0411, dtype: int64\n",
      "VAR_0412\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0412, dtype: int64\n",
      "VAR_0413\n",
      " 0.0      4961\n",
      "-999.0      38\n",
      " 1.0         1\n",
      "Name: VAR_0413, dtype: int64\n",
      "VAR_0414\n",
      " 0.0      4958\n",
      "-999.0      38\n",
      " 1.0         3\n",
      " 3.0         1\n",
      "Name: VAR_0414, dtype: int64\n",
      "VAR_0415\n",
      " 0.0      4956\n",
      "-999.0      38\n",
      " 1.0         5\n",
      " 3.0         1\n",
      "Name: VAR_0415, dtype: int64\n",
      "VAR_0416\n",
      " 0.0      4938\n",
      "-999.0      38\n",
      " 1.0        20\n",
      " 10.0        1\n",
      " 3.0         1\n",
      " 4.0         1\n",
      " 2.0         1\n",
      "Name: VAR_0416, dtype: int64\n",
      "VAR_0417\n",
      " 0.0      3886\n",
      " 1.0       396\n",
      " 2.0       311\n",
      " 3.0       110\n",
      " 4.0        88\n",
      " 5.0        47\n",
      "-999.0      38\n",
      " 6.0        37\n",
      " 7.0        28\n",
      " 9.0        14\n",
      " 8.0        13\n",
      " 10.0       10\n",
      " 13.0        5\n",
      " 14.0        4\n",
      " 11.0        4\n",
      " 12.0        2\n",
      " 19.0        2\n",
      " 21.0        1\n",
      " 44.0        1\n",
      " 31.0        1\n",
      " 17.0        1\n",
      " 22.0        1\n",
      "Name: VAR_0417, dtype: int64\n",
      "VAR_0418\n",
      " 0.0      3932\n",
      " 1.0       539\n",
      " 2.0       237\n",
      " 3.0        94\n",
      " 4.0        66\n",
      "-999.0      38\n",
      " 5.0        34\n",
      " 6.0        16\n",
      " 7.0        15\n",
      " 8.0        10\n",
      " 10.0        5\n",
      " 9.0         4\n",
      " 12.0        3\n",
      " 11.0        1\n",
      " 19.0        1\n",
      " 22.0        1\n",
      " 14.0        1\n",
      " 16.0        1\n",
      " 17.0        1\n",
      " 38.0        1\n",
      "Name: VAR_0418, dtype: int64\n",
      "VAR_0419\n",
      "-1.0      3932\n",
      "-999.0      38\n",
      " 4.0        24\n",
      " 1.0        24\n",
      " 2.0        23\n",
      "          ... \n",
      " 56.0        5\n",
      " 31.0        4\n",
      " 74.0        4\n",
      " 71.0        3\n",
      " 80.0        3\n",
      "Name: VAR_0419, Length: 87, dtype: int64\n",
      "VAR_0420\n",
      " 0.0      4934\n",
      "-999.0      38\n",
      " 1.0        26\n",
      " 2.0         2\n",
      "Name: VAR_0420, dtype: int64\n",
      "VAR_0421\n",
      " 0.0      4894\n",
      " 1.0        63\n",
      "-999.0      38\n",
      " 2.0         5\n",
      "Name: VAR_0421, dtype: int64\n",
      "VAR_0422\n",
      " 0.0      4846\n",
      " 1.0        95\n",
      "-999.0      38\n",
      " 2.0        14\n",
      " 3.0         4\n",
      " 7.0         1\n",
      " 8.0         1\n",
      " 4.0         1\n",
      "Name: VAR_0422, dtype: int64\n",
      "VAR_0423\n",
      " 0.0      4753\n",
      " 1.0       148\n",
      " 2.0        43\n",
      "-999.0      38\n",
      " 3.0         9\n",
      " 4.0         4\n",
      " 8.0         2\n",
      " 5.0         2\n",
      " 6.0         1\n",
      "Name: VAR_0423, dtype: int64\n",
      "VAR_0424\n",
      " 0.0      4582\n",
      " 1.0       247\n",
      " 2.0        78\n",
      "-999.0      38\n",
      " 3.0        30\n",
      " 4.0        13\n",
      " 5.0         5\n",
      " 6.0         3\n",
      " 8.0         2\n",
      " 10.0        1\n",
      " 7.0         1\n",
      "Name: VAR_0424, dtype: int64\n",
      "VAR_0425\n",
      " 0.0      4137\n",
      " 1.0       467\n",
      " 2.0       167\n",
      " 3.0        95\n",
      " 4.0        40\n",
      "-999.0      38\n",
      " 5.0        19\n",
      " 6.0        10\n",
      " 8.0         8\n",
      " 7.0         7\n",
      " 9.0         4\n",
      " 10.0        2\n",
      " 17.0        2\n",
      " 13.0        1\n",
      " 34.0        1\n",
      " 12.0        1\n",
      " 15.0        1\n",
      "Name: VAR_0425, dtype: int64\n",
      "VAR_0426\n",
      " 0.0      4498\n",
      " 1.0       300\n",
      " 2.0        99\n",
      "-999.0      38\n",
      " 3.0        34\n",
      " 4.0        12\n",
      " 5.0         7\n",
      " 6.0         4\n",
      " 7.0         3\n",
      " 8.0         3\n",
      " 9.0         1\n",
      " 15.0        1\n",
      "Name: VAR_0426, dtype: int64\n",
      "VAR_0427\n",
      "-1.0      4498\n",
      "-999.0      38\n",
      " 29.0       13\n",
      " 68.0       10\n",
      " 50.0       10\n",
      "          ... \n",
      " 70.0        2\n",
      " 83.0        2\n",
      " 5.0         2\n",
      " 63.0        1\n",
      " 77.0        1\n",
      "Name: VAR_0427, Length: 86, dtype: int64\n",
      "VAR_0428\n",
      " 0.0      4956\n",
      "-999.0      38\n",
      " 1.0         4\n",
      " 2.0         2\n",
      "Name: VAR_0428, dtype: int64\n",
      "VAR_0429\n",
      " 0.0      4943\n",
      "-999.0      38\n",
      " 1.0        16\n",
      " 2.0         3\n",
      "Name: VAR_0429, dtype: int64\n",
      "VAR_0430\n",
      " 0.0      4928\n",
      "-999.0      38\n",
      " 1.0        28\n",
      " 2.0         6\n",
      "Name: VAR_0430, dtype: int64\n",
      "VAR_0431\n",
      " 0.0      4898\n",
      " 1.0        54\n",
      "-999.0      38\n",
      " 2.0         9\n",
      " 3.0         1\n",
      "Name: VAR_0431, dtype: int64\n",
      "VAR_0432\n",
      " 0.0      4828\n",
      " 1.0       101\n",
      "-999.0      38\n",
      " 2.0        20\n",
      " 3.0        11\n",
      " 4.0         1\n",
      " 5.0         1\n",
      "Name: VAR_0432, dtype: int64\n",
      "VAR_0433\n",
      " 0.0      4604\n",
      " 1.0       251\n",
      " 2.0        63\n",
      "-999.0      38\n",
      " 3.0        28\n",
      " 6.0         5\n",
      " 4.0         4\n",
      " 7.0         3\n",
      " 5.0         3\n",
      " 9.0         1\n",
      "Name: VAR_0433, dtype: int64\n",
      "VAR_0434\n",
      " 0.0        4022\n",
      "-999.0        38\n",
      " 175.0         3\n",
      " 1467.0        3\n",
      " 2100.0        3\n",
      "            ... \n",
      " 1965.0        1\n",
      " 9725.0        1\n",
      " 3809.0        1\n",
      " 19625.0       1\n",
      " 3264.0        1\n",
      "Name: VAR_0434, Length: 883, dtype: int64\n",
      "VAR_0435\n",
      " 0.0          4889\n",
      "-999.0          38\n",
      " 55945.0         1\n",
      " 1216929.0       1\n",
      " 6034.0          1\n",
      "              ... \n",
      " 41765.0         1\n",
      " 13903.0         1\n",
      " 8074.0          1\n",
      " 25262.0         1\n",
      " 10045.0         1\n",
      "Name: VAR_0435, Length: 75, dtype: int64\n",
      "VAR_0436\n",
      " 0.0         4717\n",
      "-999.0         38\n",
      " 1601.0         2\n",
      " 1467.0         2\n",
      " 118.0          2\n",
      "             ... \n",
      " 132331.0       1\n",
      " 1502.0         1\n",
      " 9806.0         1\n",
      " 2897.0         1\n",
      " 544.0          1\n",
      "Name: VAR_0436, Length: 235, dtype: int64\n",
      "VAR_0437\n",
      " 0.0         4961\n",
      "-999.0         38\n",
      " 168372.0       1\n",
      "Name: VAR_0437, dtype: int64\n",
      "VAR_0438\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0438, dtype: int64\n",
      "VAR_0439\n",
      " 0.0        4464\n",
      "-999.0        38\n",
      " 1073.0        3\n",
      " 175.0         3\n",
      " 2195.0        2\n",
      "            ... \n",
      " 7404.0        1\n",
      " 3895.0        1\n",
      " 13749.0       1\n",
      " 84639.0       1\n",
      " 1683.0        1\n",
      "Name: VAR_0439, Length: 481, dtype: int64\n",
      "VAR_0440\n",
      " 0.0       4663\n",
      "-999.0       38\n",
      " 777.0        3\n",
      " 1113.0       2\n",
      " 978.0        2\n",
      "           ... \n",
      " 765.0        1\n",
      " 5861.0       1\n",
      " 1153.0       1\n",
      " 884.0        1\n",
      " 963.0        1\n",
      "Name: VAR_0440, Length: 287, dtype: int64\n",
      "VAR_0441\n",
      " 0.0        4947\n",
      "-999.0        38\n",
      " 869.0         1\n",
      " 3410.0        1\n",
      " 1856.0        1\n",
      " 2480.0        1\n",
      " 572.0         1\n",
      " 3115.0        1\n",
      " 4718.0        1\n",
      " 21310.0       1\n",
      " 1571.0        1\n",
      " 8595.0        1\n",
      " 2500.0        1\n",
      " 719.0         1\n",
      " 669.0         1\n",
      " 1553.0        1\n",
      " 1285.0        1\n",
      "Name: VAR_0441, dtype: int64\n",
      "VAR_0442\n",
      " 0.0        4501\n",
      "-999.0        38\n",
      " 200.0         2\n",
      " 279.0         2\n",
      " 133.0         2\n",
      "            ... \n",
      " 2762.0        1\n",
      " 20124.0       1\n",
      " 1007.0        1\n",
      " 1834.0        1\n",
      " 5134.0        1\n",
      "Name: VAR_0442, Length: 437, dtype: int64\n",
      "VAR_0443\n",
      " 0.0        4927\n",
      "-999.0        38\n",
      " 18214.0       1\n",
      " 5204.0        1\n",
      " 6034.0        1\n",
      " 55945.0       1\n",
      " 97521.0       1\n",
      " 31848.0       1\n",
      " 30766.0       1\n",
      " 6539.0        1\n",
      " 7688.0        1\n",
      " 10801.0       1\n",
      " 2339.0        1\n",
      " 4165.0        1\n",
      " 23010.0       1\n",
      " 21004.0       1\n",
      " 11524.0       1\n",
      " 5632.0        1\n",
      " 5397.0        1\n",
      " 6909.0        1\n",
      " 4860.0        1\n",
      " 22965.0       1\n",
      " 19686.0       1\n",
      " 11633.0       1\n",
      " 53176.0       1\n",
      " 5168.0        1\n",
      " 4208.0        1\n",
      " 11871.0       1\n",
      " 14423.0       1\n",
      " 7195.0        1\n",
      " 10838.0       1\n",
      " 79425.0       1\n",
      " 17804.0       1\n",
      " 35602.0       1\n",
      " 8894.0        1\n",
      " 10045.0       1\n",
      " 5338.0        1\n",
      "Name: VAR_0443, dtype: int64\n",
      "VAR_0444\n",
      " 0.0       4791\n",
      "-999.0       38\n",
      " 728.0        2\n",
      " 1587.0       2\n",
      " 1960.0       2\n",
      "           ... \n",
      " 1239.0       1\n",
      " 1007.0       1\n",
      " 2897.0       1\n",
      " 1010.0       1\n",
      " 1496.0       1\n",
      "Name: VAR_0444, Length: 168, dtype: int64\n",
      "VAR_0445\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0445, dtype: int64\n",
      "VAR_0446\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0446, dtype: int64\n",
      "VAR_0447\n",
      " 0.0       4749\n",
      "-999.0       38\n",
      " 3389.0       2\n",
      " 1326.0       2\n",
      " 1575.0       2\n",
      "           ... \n",
      " 4053.0       1\n",
      " 1339.0       1\n",
      " 4713.0       1\n",
      " 2201.0       1\n",
      " 184.0        1\n",
      "Name: VAR_0447, Length: 212, dtype: int64\n",
      "VAR_0448\n",
      " 0.0       4870\n",
      "-999.0       38\n",
      " 6365.0       1\n",
      " 133.0        1\n",
      " 599.0        1\n",
      "           ... \n",
      " 4459.0       1\n",
      " 3781.0       1\n",
      " 1387.0       1\n",
      " 652.0        1\n",
      " 2972.0       1\n",
      "Name: VAR_0448, Length: 94, dtype: int64\n",
      "VAR_0449\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0449, dtype: int64\n",
      "VAR_0450\n",
      " 0.0      4889\n",
      " 1.0        51\n",
      "-999.0      38\n",
      " 2.0        14\n",
      " 3.0         7\n",
      " 16.0        1\n",
      "Name: VAR_0450, dtype: int64\n",
      "VAR_0451\n",
      " 0.0      4715\n",
      " 1.0       164\n",
      " 2.0        45\n",
      "-999.0      38\n",
      " 3.0        17\n",
      " 4.0         7\n",
      " 6.0         6\n",
      " 9.0         3\n",
      " 5.0         3\n",
      " 7.0         1\n",
      " 12.0        1\n",
      "Name: VAR_0451, dtype: int64\n",
      "VAR_0452\n",
      " 0.0      4953\n",
      "-999.0      38\n",
      " 1.0         7\n",
      " 3.0         1\n",
      " 2.0         1\n",
      "Name: VAR_0452, dtype: int64\n",
      "VAR_0453\n",
      " 0.0      4877\n",
      " 1.0        68\n",
      "-999.0      38\n",
      " 2.0        11\n",
      " 3.0         2\n",
      " 6.0         2\n",
      " 14.0        1\n",
      " 9.0         1\n",
      "Name: VAR_0453, dtype: int64\n",
      "VAR_0454\n",
      " 0.0      4342\n",
      " 1.0       383\n",
      " 2.0       125\n",
      " 3.0        55\n",
      "-999.0      38\n",
      " 4.0        27\n",
      " 5.0        14\n",
      " 6.0         5\n",
      " 7.0         3\n",
      " 8.0         2\n",
      " 9.0         1\n",
      " 11.0        1\n",
      " 15.0        1\n",
      " 18.0        1\n",
      " 10.0        1\n",
      " 13.0        1\n",
      "Name: VAR_0454, dtype: int64\n",
      "VAR_0455\n",
      " 0.0      4663\n",
      " 1.0       223\n",
      "-999.0      38\n",
      " 2.0        37\n",
      " 3.0        18\n",
      " 4.0        15\n",
      " 5.0         4\n",
      " 6.0         2\n",
      "Name: VAR_0455, dtype: int64\n",
      "VAR_0456\n",
      " 0.0      4947\n",
      "-999.0      38\n",
      " 1.0        13\n",
      " 2.0         1\n",
      " 3.0         1\n",
      "Name: VAR_0456, dtype: int64\n",
      "VAR_0457\n",
      " 0.0      4927\n",
      "-999.0      38\n",
      " 1.0        24\n",
      " 2.0         7\n",
      " 3.0         3\n",
      " 4.0         1\n",
      "Name: VAR_0457, dtype: int64\n",
      "VAR_0458\n",
      " 0.0      4789\n",
      " 1.0       116\n",
      "-999.0      38\n",
      " 2.0        35\n",
      " 3.0         9\n",
      " 4.0         4\n",
      " 6.0         3\n",
      " 5.0         2\n",
      " 8.0         2\n",
      " 7.0         1\n",
      " 9.0         1\n",
      "Name: VAR_0458, dtype: int64\n",
      "VAR_0459\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0459, dtype: int64\n",
      "VAR_0460\n",
      " 0.0      4958\n",
      "-999.0      38\n",
      " 2.0         2\n",
      " 5.0         1\n",
      " 1.0         1\n",
      "Name: VAR_0460, dtype: int64\n",
      "VAR_0461\n",
      " 0.0      4749\n",
      " 1.0       159\n",
      "-999.0      38\n",
      " 2.0        37\n",
      " 3.0        11\n",
      " 6.0         2\n",
      " 5.0         2\n",
      " 10.0        1\n",
      " 4.0         1\n",
      "Name: VAR_0461, dtype: int64\n",
      "VAR_0462\n",
      " 0.0      4870\n",
      " 1.0        70\n",
      "-999.0      38\n",
      " 2.0        15\n",
      " 3.0         6\n",
      " 6.0         1\n",
      "Name: VAR_0462, dtype: int64\n",
      "VAR_0463\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0463, dtype: int64\n",
      "VAR_0464\n",
      " 0.0      4147\n",
      " 1.0       805\n",
      "-999.0      38\n",
      " 2.0         7\n",
      " 3.0         2\n",
      " 4.0         1\n",
      "Name: VAR_0464, dtype: int64\n",
      "VAR_0465\n",
      "-1.0      4147\n",
      "-999.0      38\n",
      " 87.0       17\n",
      " 80.0       17\n",
      " 79.0       16\n",
      "          ... \n",
      " 99.0        2\n",
      " 0.0         2\n",
      " 53.0        2\n",
      " 60.0        2\n",
      " 61.0        1\n",
      "Name: VAR_0465, Length: 102, dtype: int64\n",
      "VAR_0466\n",
      "-1      4152\n",
      "I        810\n",
      "-999      38\n",
      "Name: VAR_0466, dtype: int64\n",
      "VAR_0467\n",
      "-1              4161\n",
      "Discharged       749\n",
      "Dismissed         47\n",
      "-999              38\n",
      "Discharge NA       5\n",
      "Name: VAR_0467, dtype: int64\n",
      "VAR_0468\n",
      " 0.0      4954\n",
      "-999.0      38\n",
      " 1.0         8\n",
      "Name: VAR_0468, dtype: int64\n",
      "VAR_0469\n",
      " 0.0      4940\n",
      "-999.0      38\n",
      " 1.0        22\n",
      "Name: VAR_0469, dtype: int64\n",
      "VAR_0470\n",
      " 0.0      4907\n",
      " 1.0        55\n",
      "-999.0      38\n",
      "Name: VAR_0470, dtype: int64\n",
      "VAR_0471\n",
      " 0.0      4862\n",
      " 1.0       100\n",
      "-999.0      38\n",
      "Name: VAR_0471, dtype: int64\n",
      "VAR_0472\n",
      " 0.0      4752\n",
      " 1.0       209\n",
      "-999.0      38\n",
      " 2.0         1\n",
      "Name: VAR_0472, dtype: int64\n",
      "VAR_0473\n",
      " 0.0      4452\n",
      " 1.0       506\n",
      "-999.0      38\n",
      " 2.0         3\n",
      " 4.0         1\n",
      "Name: VAR_0473, dtype: int64\n",
      "VAR_0474\n",
      " 0.0      4726\n",
      " 1.0       103\n",
      " 2.0        79\n",
      "-999.0      38\n",
      " 3.0        13\n",
      " 4.0        12\n",
      " 5.0         7\n",
      " 7.0         6\n",
      " 8.0         4\n",
      " 9.0         4\n",
      " 6.0         3\n",
      " 10.0        2\n",
      " 43.0        1\n",
      " 18.0        1\n",
      " 30.0        1\n",
      "Name: VAR_0474, dtype: int64\n",
      "VAR_0475\n",
      "-1.0      4726\n",
      "-999.0      38\n",
      " 36.0        6\n",
      " 6.0         6\n",
      " 32.0        6\n",
      "          ... \n",
      " 55.0        1\n",
      " 37.0        1\n",
      " 13.0        1\n",
      " 10.0        1\n",
      " 39.0        1\n",
      "Name: VAR_0475, Length: 81, dtype: int64\n",
      "VAR_0476\n",
      " 0.0      4958\n",
      "-999.0      38\n",
      " 1.0         4\n",
      "Name: VAR_0476, dtype: int64\n",
      "VAR_0477\n",
      " 0.0      4952\n",
      "-999.0      38\n",
      " 1.0         9\n",
      " 2.0         1\n",
      "Name: VAR_0477, dtype: int64\n",
      "VAR_0478\n",
      " 0.0      4941\n",
      "-999.0      38\n",
      " 1.0        10\n",
      " 2.0         6\n",
      " 3.0         3\n",
      " 6.0         1\n",
      " 4.0         1\n",
      "Name: VAR_0478, dtype: int64\n",
      "VAR_0479\n",
      " 0.0      4918\n",
      "-999.0      38\n",
      " 2.0        20\n",
      " 1.0        19\n",
      " 3.0         1\n",
      " 7.0         1\n",
      " 4.0         1\n",
      " 6.0         1\n",
      " 5.0         1\n",
      "Name: VAR_0479, dtype: int64\n",
      "VAR_0480\n",
      " 0.0      4884\n",
      "-999.0      38\n",
      " 1.0        37\n",
      " 2.0        29\n",
      " 3.0         5\n",
      " 4.0         3\n",
      " 7.0         2\n",
      " 6.0         1\n",
      " 8.0         1\n",
      "Name: VAR_0480, dtype: int64\n",
      "VAR_0481\n",
      " 0.0      4788\n",
      " 1.0        78\n",
      " 2.0        57\n",
      "-999.0      38\n",
      " 3.0        13\n",
      " 4.0        10\n",
      " 8.0         5\n",
      " 7.0         3\n",
      " 9.0         2\n",
      " 6.0         2\n",
      " 5.0         2\n",
      " 33.0        1\n",
      " 14.0        1\n",
      "Name: VAR_0481, dtype: int64\n",
      "VAR_0482\n",
      " 4.0      2378\n",
      " 3.0      1721\n",
      " 2.0       362\n",
      " 5.0       309\n",
      " 6.0       133\n",
      " 1.0        41\n",
      "-999.0      38\n",
      " 7.0        10\n",
      "-1.0         8\n",
      "Name: VAR_0482, dtype: int64\n",
      "VAR_0483\n",
      " 3.0      1578\n",
      " 2.0      1263\n",
      " 4.0      1044\n",
      " 1.0       548\n",
      " 5.0       400\n",
      " 6.0        94\n",
      "-999.0      38\n",
      " 7.0        23\n",
      " 0.0         8\n",
      " 8.0         3\n",
      " 9.0         1\n",
      "Name: VAR_0483, dtype: int64\n",
      "VAR_0484\n",
      " 1.0      2510\n",
      " 2.0      1950\n",
      " 3.0       429\n",
      " 0.0        39\n",
      "-999.0      38\n",
      " 4.0        31\n",
      " 5.0         3\n",
      "Name: VAR_0484, dtype: int64\n",
      "VAR_0485\n",
      " 1.0      2434\n",
      " 2.0      1979\n",
      " 3.0       474\n",
      "-999.0      38\n",
      " 4.0        38\n",
      " 0.0        34\n",
      " 5.0         3\n",
      "Name: VAR_0485, dtype: int64\n",
      "VAR_0486\n",
      " 1.0      2295\n",
      " 2.0      2040\n",
      " 3.0       541\n",
      " 4.0        54\n",
      "-999.0      38\n",
      " 0.0        28\n",
      " 5.0         4\n",
      "Name: VAR_0486, dtype: int64\n",
      "VAR_0487\n",
      " 2.0      2079\n",
      " 1.0      2047\n",
      " 3.0       721\n",
      " 4.0        87\n",
      "-999.0      38\n",
      " 0.0        23\n",
      " 5.0         5\n",
      "Name: VAR_0487, dtype: int64\n",
      "VAR_0488\n",
      " 2.0      2109\n",
      " 1.0      1702\n",
      " 3.0       944\n",
      " 4.0       180\n",
      "-999.0      38\n",
      " 0.0        19\n",
      " 5.0         8\n",
      "Name: VAR_0488, dtype: int64\n",
      "VAR_0489\n",
      " 2.0      1860\n",
      " 3.0      1437\n",
      " 1.0      1070\n",
      " 4.0       502\n",
      " 5.0        66\n",
      "-999.0      38\n",
      " 0.0        13\n",
      " 6.0        12\n",
      " 8.0         1\n",
      " 7.0         1\n",
      "Name: VAR_0489, dtype: int64\n",
      "VAR_0490\n",
      " 0.0      3221\n",
      " 1.0      1741\n",
      "-999.0      38\n",
      "Name: VAR_0490, dtype: int64\n",
      "VAR_0491\n",
      " 0.0      4875\n",
      " 1.0        73\n",
      "-999.0      38\n",
      " 2.0        12\n",
      " 4.0         1\n",
      " 3.0         1\n",
      "Name: VAR_0491, dtype: int64\n",
      "VAR_0492\n",
      "-1.0      4673\n",
      "-999.0      38\n",
      " 3.0        11\n",
      " 39.0       10\n",
      " 1.0        10\n",
      "          ... \n",
      " 26.0        1\n",
      " 52.0        1\n",
      " 5.0         1\n",
      " 13.0        1\n",
      " 91.0        1\n",
      "Name: VAR_0492, Length: 87, dtype: int64\n",
      "VAR_0493\n",
      "-1                          4673\n",
      "-999                          38\n",
      "REGISTERED NURSE              34\n",
      "LICENSED PRACTICAL NURSE      24\n",
      "COSMETOLOGIST                 19\n",
      "                            ... \n",
      "WATER OPERATOR CLASS 4         1\n",
      "PHARMACIST                     1\n",
      "MENTAL HEALTH COUNSELOR        1\n",
      "ELECTRICIANS                   1\n",
      "COSMETICIAN                    1\n",
      "Name: VAR_0493, Length: 107, dtype: int64\n",
      "VAR_0494\n",
      "-1.0      4673\n",
      " 4.0        80\n",
      " 1.0        71\n",
      " 3.0        48\n",
      " 2.0        47\n",
      "-999.0      38\n",
      " 0.0        31\n",
      " 5.0        12\n",
      "Name: VAR_0494, dtype: int64\n",
      "VAR_0495\n",
      "-1.0      4875\n",
      " 1.0        55\n",
      "-999.0      38\n",
      " 0.0        32\n",
      "Name: VAR_0495, dtype: int64\n",
      "VAR_0496\n",
      " 0.0      4948\n",
      "-999.0      38\n",
      " 1.0        13\n",
      " 3.0         1\n",
      "Name: VAR_0496, dtype: int64\n",
      "VAR_0497\n",
      " 0.0      4929\n",
      "-999.0      38\n",
      " 1.0        31\n",
      " 2.0         1\n",
      " 3.0         1\n",
      "Name: VAR_0497, dtype: int64\n",
      "VAR_0498\n",
      " 0.0      4910\n",
      " 1.0        50\n",
      "-999.0      38\n",
      " 2.0         1\n",
      " 3.0         1\n",
      "Name: VAR_0498, dtype: int64\n",
      "VAR_0499\n",
      " 0.0      4892\n",
      " 1.0        65\n",
      "-999.0      38\n",
      " 2.0         4\n",
      " 3.0         1\n",
      "Name: VAR_0499, dtype: int64\n",
      "VAR_0500\n",
      " 0.0      4853\n",
      " 1.0        94\n",
      "-999.0      38\n",
      " 2.0        13\n",
      " 3.0         2\n",
      "Name: VAR_0500, dtype: int64\n",
      "VAR_0501\n",
      " 0.0      4731\n",
      " 1.0       153\n",
      " 2.0        54\n",
      "-999.0      38\n",
      " 3.0        15\n",
      " 4.0         5\n",
      " 5.0         2\n",
      " 10.0        1\n",
      " 6.0         1\n",
      "Name: VAR_0501, dtype: int64\n",
      "VAR_0502\n",
      " 0.0      4959\n",
      "-999.0      38\n",
      " 1.0         3\n",
      "Name: VAR_0502, dtype: int64\n",
      "VAR_0503\n",
      " 0.0      4364\n",
      " 1.0       598\n",
      "-999.0      38\n",
      "Name: VAR_0503, dtype: int64\n",
      "VAR_0504\n",
      " 0.0      3845\n",
      " 1.0      1117\n",
      "-999.0      38\n",
      "Name: VAR_0504, dtype: int64\n",
      "VAR_0505\n",
      " 0.0      4012\n",
      " 1.0       950\n",
      "-999.0      38\n",
      "Name: VAR_0505, dtype: int64\n",
      "VAR_0506\n",
      " 0.0      4475\n",
      " 1.0       342\n",
      " 2.0        85\n",
      "-999.0      38\n",
      " 3.0        33\n",
      " 4.0        15\n",
      " 5.0         5\n",
      " 7.0         3\n",
      " 6.0         2\n",
      " 8.0         2\n",
      "Name: VAR_0506, dtype: int64\n",
      "VAR_0507\n",
      " 0.0      4952\n",
      "-999.0      38\n",
      " 1.0        10\n",
      "Name: VAR_0507, dtype: int64\n",
      "VAR_0508\n",
      " 0.0      4939\n",
      "-999.0      38\n",
      " 1.0        23\n",
      "Name: VAR_0508, dtype: int64\n",
      "VAR_0509\n",
      " 0.0      4912\n",
      "-999.0      38\n",
      " 1.0        37\n",
      " 2.0         9\n",
      " 3.0         4\n",
      "Name: VAR_0509, dtype: int64\n",
      "VAR_0510\n",
      " 0.0      4860\n",
      " 1.0        76\n",
      "-999.0      38\n",
      " 2.0        18\n",
      " 3.0         7\n",
      " 5.0         1\n",
      "Name: VAR_0510, dtype: int64\n",
      "VAR_0511\n",
      " 0.0      4723\n",
      " 1.0       176\n",
      "-999.0      38\n",
      " 2.0        35\n",
      " 3.0        20\n",
      " 4.0         5\n",
      " 6.0         2\n",
      " 5.0         1\n",
      "Name: VAR_0511, dtype: int64\n",
      "VAR_0512\n",
      " 0.0      4475\n",
      " 1.0       342\n",
      " 2.0        85\n",
      "-999.0      38\n",
      " 3.0        33\n",
      " 4.0        15\n",
      " 5.0         5\n",
      " 7.0         3\n",
      " 6.0         2\n",
      " 8.0         2\n",
      "Name: VAR_0512, dtype: int64\n",
      "VAR_0513\n",
      " 1.0      2817\n",
      " 0.0      2103\n",
      "-1.0        42\n",
      "-999.0      38\n",
      "Name: VAR_0513, dtype: int64\n",
      "VAR_0514\n",
      "-1.0      1969\n",
      " 59.0       63\n",
      " 93.0       55\n",
      " 72.0       55\n",
      " 95.0       53\n",
      "          ... \n",
      " 139.0       3\n",
      " 123.0       3\n",
      " 151.0       3\n",
      " 153.0       2\n",
      " 126.0       1\n",
      "Name: VAR_0514, Length: 156, dtype: int64\n",
      "VAR_0515\n",
      "-1.0      1969\n",
      " 0.0      1410\n",
      "-999.0      38\n",
      " 8.0        36\n",
      " 1.0        31\n",
      "          ... \n",
      " 88.0        2\n",
      " 89.0        2\n",
      " 94.0        2\n",
      " 92.0        1\n",
      " 91.0        1\n",
      "Name: VAR_0515, Length: 96, dtype: int64\n",
      "VAR_0516\n",
      "-1.0      1865\n",
      " 64.0       81\n",
      " 68.0       71\n",
      " 66.0       66\n",
      " 61.0       65\n",
      "          ... \n",
      " 193.0       1\n",
      " 177.0       1\n",
      " 186.0       1\n",
      " 123.0       1\n",
      " 130.0       1\n",
      "Name: VAR_0516, Length: 160, dtype: int64\n",
      "VAR_0517\n",
      "-1.0      1865\n",
      " 0.0      1274\n",
      " 18.0       54\n",
      " 9.0        54\n",
      " 13.0       51\n",
      "          ... \n",
      " 91.0        1\n",
      " 105.0       1\n",
      " 76.0        1\n",
      " 101.0       1\n",
      " 240.0       1\n",
      "Name: VAR_0517, Length: 110, dtype: int64\n",
      "VAR_0518\n",
      " 0.0      4920\n",
      "-1.0        42\n",
      "-999.0      38\n",
      "Name: VAR_0518, dtype: int64\n",
      "VAR_0519\n",
      " 0.0      4773\n",
      " 3.0        87\n",
      "-1.0        42\n",
      "-999.0      38\n",
      " 1.0        27\n",
      " 4.0        22\n",
      " 5.0        10\n",
      " 2.0         1\n",
      "Name: VAR_0519, dtype: int64\n",
      "VAR_0520\n",
      " 1.0      3653\n",
      " 0.0      1307\n",
      "-999.0      38\n",
      "-1.0         2\n",
      "Name: VAR_0520, dtype: int64\n",
      "VAR_0521\n",
      " 0.0      4956\n",
      "-999.0      38\n",
      " 1.0         6\n",
      "Name: VAR_0521, dtype: int64\n",
      "VAR_0522\n",
      " 0.0      4868\n",
      "-1.0        94\n",
      "-999.0      38\n",
      "Name: VAR_0522, dtype: int64\n",
      "VAR_0523\n",
      " 0.0      4748\n",
      "-1.0       214\n",
      "-999.0      38\n",
      "Name: VAR_0523, dtype: int64\n",
      "VAR_0524\n",
      " 0.0      4957\n",
      "-999.0      38\n",
      " 1.0         3\n",
      "-1.0         2\n",
      "Name: VAR_0524, dtype: int64\n",
      "VAR_0525\n",
      " 0.0      4753\n",
      " 3.0       190\n",
      "-999.0      38\n",
      " 1.0        15\n",
      " 2.0         4\n",
      "Name: VAR_0525, dtype: int64\n",
      "VAR_0526\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0526, dtype: int64\n",
      "VAR_0527\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0527, dtype: int64\n",
      "VAR_0528\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0528, dtype: int64\n",
      "VAR_0529\n",
      " 0.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0529, dtype: int64\n",
      "VAR_0530\n",
      "-1.0      4962\n",
      "-999.0      38\n",
      "Name: VAR_0530, dtype: int64\n",
      "VAR_0531\n",
      " 201210.0    493\n",
      " 201205.0    490\n",
      " 201208.0    465\n",
      " 201110.0    451\n",
      " 201207.0    436\n",
      " 201206.0    432\n",
      " 201209.0    413\n",
      " 201111.0    394\n",
      " 201204.0    374\n",
      " 201112.0    367\n",
      " 201203.0    233\n",
      " 201201.0    213\n",
      " 201202.0    201\n",
      "-999.0        38\n",
      "Name: VAR_0531, dtype: int64\n",
      "VAR_0840\n",
      "-999    5000\n",
      "Name: VAR_0840, dtype: int64\n",
      "VAR_1934\n",
      "BRANCH    2330\n",
      "IAPS      2328\n",
      "MOBILE     118\n",
      "CSC        115\n",
      "RCC        109\n",
      "Name: VAR_1934, dtype: int64\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "source": [
    "# VAR_0073 是日期\n",
    "# 日期转换\n",
    "pd.to_datetime(X_train['VAR_0073'],format = '%d%b%y:%H:%M:%S', errors='coerce')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0             NaT\n",
       "1      2012-09-04\n",
       "2             NaT\n",
       "3             NaT\n",
       "4             NaT\n",
       "          ...    \n",
       "4995          NaT\n",
       "4996          NaT\n",
       "4997          NaT\n",
       "4998          NaT\n",
       "4999          NaT\n",
       "Name: VAR_0073, Length: 5000, dtype: datetime64[ns]"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "metadata": {}
  }
 ],
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